Abstract

Disclosed herein are system, method, and computer program product embodiments for ride hailing an autonomous vehicle by a third party. For example, the method includes: receiving, from a user device associated with a user account of a user for an autonomous vehicle service, (i) a request to add a rider profile associated with a rider other than the user to the user account and (ii) rider information associated with the rider; generating, based on the rider information, the rider profile; receiving, from the user device, a pick-up request associated with (i) the user account and (ii) the rider profile; assigning, based on the pick-up request, an autonomous vehicle to pick-up the rider; and providing, to the autonomous vehicle assigned to pick-up the rider, based on the rider profile, an indication of a type of identification to use to identify the rider and/or unlock the autonomous vehicle when picking-up the rider.

IPC Classes  ?

• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 50/40 - Business processes related to the transportation industry
• G06Q 50/47 - Passenger ride requests, e.g. ride-hailing

Abstract

Disclosed herein are system, method, and computer program product embodiments for autoscaling parameters of a computing environment. The methods include receiving a system metric that relates to usage of a computing resource associated with an application executed within the computing environment, and determining whether the system metric is within a desired operating range. When the system metric is determined to be not within the desired operating range, the methods also include determining a scaling rule for autoscaling one or more parameters of the computing environment based on a number of currently utilized computing resources, and autoscaling the one or more parameters of the computing environment to bring the system metric within the desired operating range.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation

Abstract

Provided are systems, methods, and computer program products for streaming data mining with text-image joint embeddings by obtaining a roadway dataset, having a condition that each record in the roadway dataset includes an image logged by an autonomous vehicle (AV) in a roadway, generating based on a text-image model, an embedding dataset comprising an image embedding for each record in the roadway dataset, each image embedding identifying a point in a high dimension space, determining, by the one or more processors, a parametric description of a high probability field which surrounds a query input text in the high dimension space, determining, by the one or more processors, at least one input image of an input stream of images that is in the high probability field, and automatically labeling, by the one or more processors, the at least one input image based on the query input text.

IPC Classes  ?

• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations

Abstract

Disclosed herein are system, method, and computer program product embodiments for ride hailing an autonomous vehicle by a third party. For example, the method includes: receiving, from a user device associated with a user account of a user for an autonomous vehicle service, (i) a request to add a rider profile associated with a rider other than the user to the user account and (ii) rider information associated with the rider; generating, based on the rider information, the rider profile; receiving, from the user device, a pick-up request associated with (i) the user account and (ii) the rider profile; assigning, based on the pick-up request, an autonomous vehicle to pick-up the rider; and providing, to the autonomous vehicle assigned to pick-up the rider, based on the rider profile, an indication of a type of identification to use to identify the rider and/or unlock the autonomous vehicle when picking-up the rider.

IPC Classes  ?

• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 50/40 - Business processes related to the transportation industry
• G06Q 50/47 - Passenger ride requests, e.g. ride-hailing

Abstract

This document discloses system, method, and computer program product embodiments for preparing a fleet of vehicles for service. For example, the method includes receiving: a set of mission types, a set of mission requests, vehicle information related to vehicles of a fleet, and personnel information. The method further includes applying the set of mission types and the set of mission requests to one or more priority engines to produce a prioritized list of mission requests. The method further includes applying the received vehicle information, the received personnel information, and the prioritized list of mission requests to one or more rules engines to produce a proposed schedule of missions for a period of time, generating software and/or map data for one or more of the vehicles, and providing the generated software and/or map data to the vehicles prior to the period of time.

IPC Classes  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06Q 50/40 - Business processes related to the transportation industry

Abstract

An adapter device for use with automotive Ethernet may include a power connector, a universal serial bus type-c (USB-C) connector, an Ethernet controller, and a power switching circuit, when the power switching circuit is in a first state, the power switching circuit is configured to prevent power from being received from a USB-C device connected via the USB-C connector, when the power switching circuit is in a second state, the power switching circuit is configured to allow power to be received from a USB-C device connected via the USB-C connector, and the Ethernet controller is configured to convert a first signal received via the USB-C connector to a second signal that is formatted for automotive Ethernet. Methods, computer program products, and autonomous vehicles are also disclosed.

IPC Classes  ?

• H04L 12/10 - Current supply arrangements
• G06F 1/18 - Packaging or power distribution
• G06F 13/38 - Information transfer, e.g. on bus

Abstract

Disclosed herein are systems, methods, and computer program products for providing an electronic map. The methods comprise: identifying at least one first data object that was changed during a last update of the electronic map; creating at least one geometry artifact by converting a format of data associated with the at least one first data object from a first data format to a different second data format (wherein the geometry artifact comprises 2D grid coordinates for a point, a line or a polygon); using the geometry artifact to generate first vector tile(s) in the different second data format; and selectively providing the first map in the first data format or the first vector tile(s) in the second data format based on characteristics of a software application or online service requesting access to contents of the electronic map.

IPC Classes  ?

• G06F 16/29 - Geographical information databases
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/25 - Integrating or interfacing systems involving database management systems

Abstract

Disclosed herein are system, method, and computer program product embodiments for handling unmapped speed limit signs. For example, the method includes receiving sensor data from a sensor of a vehicle, identifying a traffic sign within a field of view of the vehicle based on the sensor data, identifying one or more lane segments of a road associated with the identified traffic sign based on a determination that the identified traffic sign is not mapped in a priori map, and updating an attribute associated with a segment of the one or more lane segments of the road based on a determination that a detected attribute corresponding to the identified sign is more restrictive than an attribute associated with the lane segment in the a priori map.

IPC Classes  ?

• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G01C 21/34 - Route searchingRoute guidance
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
• G08G 1/0962 - Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
• G08G 1/0967 - Systems involving transmission of highway information, e.g. weather, speed limits
• G08G 1/0968 - Systems involving transmission of navigation instructions to the vehicle

Abstract

A method for controlling a driving behavior of an autonomously driving vehicle, wherein a processing device performs the following steps for at least one region of a road network: receiving trace data of the region, wherein the trace data describe at least one trace of historic movements and/or behaviors of past traffic participants in the region, wherein the movements and/or behaviors are incompatible with and/or not anticipated by traffic regulations currently valid in the region; deriving rule data describing regular movements and/or behaviors of the past traffic participants described by the trace data; and providing the rule data to a motion model of the vehicle, wherein the vehicle comprises a driving control system (DCS) that plans and/or adapts a driving trajectory by detecting a traffic participant in the region and predicting a future behavior of the participant using the motion model.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles

Abstract

Methods and systems for controlling navigation of an autonomous vehicle for traversing a geographical area are disclosed. The methods include receiving information relating to a drivable area in the geographical area and identifying a plurality of lane segments that intersect with the drivable area. The plurality of lane segments can be used to segment the drivable area into a plurality of sub-regions such that each of the plurality of lane segments can be represented as a union of a unique subset of the plurality of sub-regions. A data representation of the drivable area may be created to include the plurality of sub-regions, and used to render the map of the geographical area. The map includes the drivable area and/or one or more of the plurality of lane segments.

IPC Classes  ?

• G06T 17/20 - Wire-frame description, e.g. polygonalisation or tessellation
• B60W 30/18 - Propelling the vehicle
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• G06F 16/29 - Geographical information databases
• G06T 7/13 - Edge detection
• G06T 17/05 - Geographic models
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Abstract

Disclosed herein are system, method, and computer program product embodiments for determining a precipitation rate. In some embodiments, a processor receives precipitation data from an array of sensors of a set of array of sensors disposed on a vehicle. A set of lidar sensors are disposed on the vehicle and the array of sensors is positioned within a predetermined distance of a respective lidar sensor of the set of lidar sensors. The processor generates a precipitation rate based on an average of the precipitation data received from the array of sensors and trains a model to control a function of the vehicle during precipitation using the precipitation rate.

IPC Classes  ?

• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• G01S 17/95 - Lidar systems, specially adapted for specific applications for meteorological use

Abstract

Provided are systems, methods, and computer program products for protecting AV communications including a sender component and a receiver component from components of an AV system communicating via a shared memory buffer, the sender component configured to send one or more serialized communications to the receiver component, by controlling at least one processor to access a data block storing message data; obtain a first instruction for serializing a communication; obtain a second instruction for computing a CRC checksum; and interleave the CRC checksum with serialized message data to generate a communication within a communication channel, by computing a serialized communication of the message data in the data block based on the first instruction, while concurrently computing the CRC checksum for the message data based on the second instruction.

IPC Classes  ?

• H03M 13/09 - Error detection only, e.g. using cyclic redundancy check [CRC] codes or single parity bit

Abstract

Disclosed herein are system, method, and computer program product embodiments for handling changes in road markings. For example, the method includes identifying a road marking in a road trajectory of a vehicle using an artificial intelligence (AI) model and sensor data from a sensor of the vehicle and performing a map update based on a determination that there is a change in road markings in the road trajectory. The determination is based on at least on-board data generated when the vehicle is traversing the road trajectory and off-board data generated using stored data. The sensor data includes two or more sensor modalities.

IPC Classes  ?

• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods

Abstract

Disclosed herein are system, method, and computer program product aspects for enabling providing map updates to an autonomous vehicle (AV). The system can has four features that can (1) provide live map updates, (2) provide a mechanism to optimize map updates, (3) utilize AV sensors to provide local map updates and integrate into the broader map update system, and (4) provide a mechanism to provide low latency updates.

IPC Classes  ?

• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G06F 16/29 - Geographical information databases

Abstract

Disclosed herein are system, method, and computer program product aspects for enabling providing map updates to an autonomous vehicle (AV). The system can has four features that can (1) provide live map updates, (2) provide a mechanism to optimize map updates, (3) utilize AV sensors to provide local map updates and integrate into the broader map update system, and (4) provide a mechanism to provide low latency updates.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/00 - NavigationNavigational instruments not provided for in groups

Abstract

Disclosed herein are system, method, and computer program product aspects for enabling providing map updates to an autonomous vehicle (AV). The system can has four features that can (1) provide live map updates, (2) provide a mechanism to optimize map updates, (3) utilize AV sensors to provide local map updates and integrate into the broader map update system, and (4) provide a mechanism to provide low latency updates.

IPC Classes  ?

• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles

Abstract

Disclosed herein are system, method, and computer program product aspects for simulating map update functions in an autonomous vehicle (AV) system. The system can create a simulation environment to test the AV system using data collected during regular operations of the AV system. The data comprises (1) map updating documents from a cloud service, (2) local map updating documents, and (3) observation data. A map-deviation detection mechanism is used to process the observation data.

IPC Classes  ?

• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G01C 21/32 - Structuring or formatting of map data

Abstract

Disclosed herein are system, method, and computer program product aspects for enabling providing map updates to an autonomous vehicle (AV). The system can has four features that can (1) provide live map updates, (2) provide a mechanism to optimize map updates, (3) utilize AV sensors to provide local map updates and integrate into the broader map update system, and (4) provide a mechanism to provide low latency updates.

IPC Classes  ?

• G06F 16/23 - Updating
• G06F 16/29 - Geographical information databases

Abstract

Disclosed herein are system, method, and computer program product embodiments for a mobile offload station for disconnected terminal operation. For example, the method includes deploying at least one autonomous vehicle (AV) to within a predetermined proximity of a mobile offload station responsive to log data stored on a data storage device onboard the AV exceeding a data storage threshold. The method further includes establishing, by the at least one AV, a data communication link with the mobile offload station responsive to entry into the predetermined proximity. The method further includes transmitting, by the at least one AV, to a storage device onboard the mobile offload station, at least a portion of the log data via the data communication link.

IPC Classes  ?

• H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
• H04W 4/02 - Services making use of location information
• H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

Disclosed herein are system, method, and computer program product embodiments for a mobile offload station for disconnected terminal operation. For example, the method includes deploying a mobile offload station to within a predetermined proximity of an autonomous vehicle (AV) responsive to receipt of an instruction to ingest log data stored on a data storage device onboard the AV. The method further includes establishing, by the mobile offload station, a data communication link with the AV responsive to entry into the predetermined proximity. The method further includes storing, by the mobile offload station, on a storage device onboard the mobile offload station, the log data transferred via the data communication link from the data storage device of the AV.

IPC Classes  ?

• B60W 50/04 - Monitoring the functioning of the control system
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 11/30 - Monitoring

Abstract

Systems, methods, and computer-program products for video encoding for autonomous vehicles are disclosed. A method for video encoding for autonomous vehicles may include receiving image data associated with a plurality of images from at least one camera of an autonomous vehicle. The image data may be encoded into a plurality of video streams including at least one first video stream and at least one second video stream. The at least one first video stream may be associated with a first quality level. The at least one second video stream may be associated with a second quality level different than the first quality level. At least one of the plurality of video streams may be communicated to at least one data storage device.

IPC Classes  ?

• H04N 19/423 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation characterised by memory arrangements
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• H04N 19/136 - Incoming video signal characteristics or properties
• H04N 19/436 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation using parallelised computational arrangements

Abstract

This document discloses system, method, and computer program product embodiments for dynamically assigning vehicles or other objects to fleets of multiple tenants. Each tenant will be assigned a primary fleet of objects (such as vehicles) and will be associated with a minimum service level requirement and parameters governing operation of each object that is assigned to that primary fleet. The system will maintain a common fleet of vehicles, from which objects may be temporarily assigned to the primary fleets. When one of the tenants submits a service request, the system will select an object from the common fleet, assign the selected object to the primary fleet of that tenant's primary fleet, and cause the object to fulfill the first trip request in accordance with the set of parameters governing operation of each object that is assigned to the primary fleet of that tenant.

IPC Classes  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 50/30 - Transportation; Communications

Abstract

Disclosed herein are methods, systems, and computer program products for automated delivery of goods that include: a deployment vehicle; and an autonomous delivery vehicle contained within the deployment vehicle, where the delivery vehicle secures a package, where the delivery vehicle is programmed or configured to: deploy the delivery vehicle from the deployment vehicle; autonomously navigate the delivery vehicle from the deployment vehicle to a delivery location; park the delivery vehicle at the delivery location; and in response to an authorization protocol being satisfied, release the package.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60R 25/24 - Means to switch the anti-theft system on or off using electronic identifiers containing a code not memorised by the user
• G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management

Abstract

Disclosed herein are system, method, and computer program product embodiments for an asymmetrical Autonomous Vehicle Systems (AVS). A backup AVS is implemented on a vehicle to serve as a failover system for one or more of the primary AVS components or processes (e.g., steering, braking, etc.). In this way, during primary AVS failures, the backup AVS can dynamically handle a subset of vehicle operations in various component configuration levels based on a desired mission level.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 50/023 - Avoiding failures by using redundant parts

Abstract

Disclosed herein are system, method, and computer program product embodiments for automated autonomous vehicle pose validation. An embodiment operates by generating a range image from a point cloud solution comprising a pose estimate for an autonomous vehicle. The embodiment queries the range image for predicted ranges and predicted class labels corresponding to lidar beams projected into the range image. The embodiment generates a vector of features from the range image. The embodiment compares a plurality of values to the vector of features using a binary classifier. The embodiment validates the autonomous vehicle pose based on the comparison of the plurality of values to the vector of features using the binary classifier.

IPC Classes  ?

• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01S 7/48 - Details of systems according to groups , , of systems according to group
• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods

Abstract

The invention is concerned with a method for generating encoded training data, wherein a processing unit performs the following steps for generating the encoded training data (11) and for storing them into a data storage during a test drive of a test vehicle that carries a camera that is generating raw image data. It is tested, if a given video encoder is suitable for encoding the raw image data for generating the encoded training data for a training of another, second artificial neural network, by performing a testing procedure.

IPC Classes  ?

• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• G06T 9/00 - Image coding
• G06V 10/60 - Extraction of image or video features relating to illumination properties, e.g. using a reflectance or lighting model

Abstract

This document discloses system, method, and computer program product embodiments for managing data generated by one or more systems of a vehicle. In various embodiments, a processor onboard a vehicle receives messages generated by one or more onboard systems of the vehicle. The system saves a first set of the messages to a first storage location on the vehicle according to a first data logging policy. The system processes a second set of the messages to reduce data elements and yield offboard data that is designated for offboard use. The first and second sets of messages may or may not overlap with each other. The system saves the offboard data to a second storage location that is onboard the vehicle and subject to a second data logging policy. The second data logging policy differs from the first data logging policy.

IPC Classes  ?

• G07C 5/00 - Registering or indicating the working of vehicles
• G07C 5/12 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time in graphical form

Abstract

Disclosed herein are system, method, and computer program product embodiments for detecting anomalies during software testing. The methods include generating a plurality of test reports for the software program by executing one or more test cases on a plurality of versions of the software program, generating a control chart based on the plurality of test reports, generating an alert when at least one testing characteristic includes an anomaly over the plurality of versions of the software program as determined based on the control chart. The control chart includes a plot associated with at least one testing characteristic of the software program, and a historical context associated with execution of the one or more test cases on the plurality of versions of the software program.

IPC Classes  ?

• G06F 11/3668 - Testing of software
• G06F 11/32 - Monitoring with visual indication of the functioning of the machine
• G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
• G06F 11/3698 - Environments for analysis, debugging or testing of software
• G06F 8/71 - Version control Configuration management
• G06F 8/77 - Software metrics
• G06F 9/445 - Program loading or initiating
• G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines
• G06F 11/22 - Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
• G06F 11/263 - Generation of test inputs, e.g. test vectors, patterns or sequences
• G06F 11/3604 - Analysis of software for verifying properties of programs
• G06F 11/362 - Debugging of software
• G06F 16/23 - Updating
• G06F 16/25 - Integrating or interfacing systems involving database management systems

Abstract

The invention is concerned with a method for controlling a vehicle (10) when an obstacle (18) is detected in surroundings (39) of the vehicle (10), wherein an autonomous driving function of the vehicle (10) plans a trajectory (15). An observer function (23) determines and/or adapts a size of a dynamic protection zone (26) that extends in a current driving direction (13), wherein the size depends on a current driving speed of the vehicle (10), wherein the observer function determines whether the obstacle (18) is detected within the dynamic protection zone (26), and in the case the obstacle (18) is detected, a limiter function (29) is signaled by the observer function, wherein the limiter function (29) is provided in the signal path (30) and the limiter function (29) reduces speed values of the planned trajectory (15) without influencing the line (25) of the movement of the vehicle (10), thereby causing the observer function (23) to shrink the dynamic protection zone (26) until the obstacle (18) lies outside the shrunken dynamic protection zone (26).

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G08G 1/16 - Anti-collision systems
• B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering

Abstract

A method for reconstructing surface topology of a ground surface in an environment of a vehicle. The reconstruction is based on LIDAR sensor scan data. A virtual horizontal grid is defined for dividing the environment into distinct grid cells. In each grid cell, a local tile is defined as a function of coordinates of those measurement points that are contained in the respective grid cell. The respective local tile of each individual grid cell is classified as belonging to the ground surface based on reference surface data that identifies the respective local tile of at least one grid cell as a ground surface tile. For at least some grid cells, a respective height value of the measurement points in that grid cell is re-calculated as being the vertical distance of the respective measurement point above the ground surface, as it is defined by the identified ground surface tiles.

IPC Classes  ?

• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G01S 17/42 - Simultaneous measurement of distance and other coordinates
• G01S 7/48 - Details of systems according to groups , , of systems according to group

Abstract

Systems and methods for controlling an autonomous vehicle (AV). The methods comprise: generating candidate intentions of an actor based on a detected action of the actor and a classification associated with the actor; determining an overall probability for each candidate intention based on at least a persistence of the candidate intention over a non-interrupted sequence of cycles (where each cycle represents a time period over which the actor was sensed by a sensor); selecting candidate intention(s) based on the overall probabilities; forecasting a subsequent future intention that the actor may have after reaching a goal defined by the candidate intention(s) which was(were) selected; obtaining an actor trajectory that is consistent with the candidate intention(s) which was(were) selected and the subsequent future intention; and using the actor trajectory to influence a selected trajectory for AV.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting

Abstract

This document discloses system, method, and computer program product embodiments for determining an intermediate (i.e., alternate) stopping location (ISL) for a ride service request when a desired stopping location (DSL) is not reachable. The system will map and sensor data to select an ISL. In response to determining that the passenger has approved the ISL as a final stopping location (FSL), the vehicle will move along a route to the FSL.

IPC Classes  ?

• G01C 21/34 - Route searchingRoute guidance
• G01C 21/36 - Input/output arrangements for on-board computers
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 50/30 - Transportation; Communications

Abstract

Methods and systems for controlling navigation of an autonomous vehicle for traversing a drivable area are disclosed. The methods include receiving information relating to a drivable area that includes a plurality of polygons, identifying a plurality of logical edges that form a boundary of the drivable area, sequentially and repeatedly analyzing concavities of each the plurality of logical edges until identification of a first logical edge that has a concavity greater than a threshold, creating a first logical segment of the boundary of the drivable area. This segmentation may be repeated until each of the plurality of logical edges has been classified. The method may include creating and adding (to a map) a data representation of the drivable area that comprises an indication of the plurality of logical segments, and adding the data representation to a road network map comprising the drivable area.

IPC Classes  ?

• G05D 1/617 - Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G01C 21/34 - Route searchingRoute guidance
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• G08G 1/16 - Anti-collision systems

Abstract

Disclosed herein are system, method, and computer program product embodiments for clustering lane segments of a roadway in order to improve and simplify autonomous vehicle behavior testing. The approaches disclosed herein provide a hybrid methodology of dividing lane segments into hard features and soft features, and using a metric learning model trained in a supervised process on the entirety of lane segment features to cluster the lane segments based on the soft features. These clustered lane segments can then be assigned to what is termed as protolanes, where a single set of tests applied to a given protolane is considered valid across all of the lane segments assigned to the protolane.

IPC Classes  ?

• G06F 18/23 - Clustering techniques
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/34 - Route searchingRoute guidance
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Abstract

Methods and systems for determining whether a camera of an autonomous vehicle (AV) is calibrated are disclosed. The method includes determining a relative positional range for a calibration target with respect to the AV, capturing a plurality of images of the calibration target, using the camera when the calibration target and the AV are positioned within the relative positional range, measuring a camera-based calibration factor and a motion-based validation factor based on the plurality of images for generating a confidence score, and generating a signal indicating that the camera is not calibrated when the confidence score is below a threshold.

IPC Classes  ?

• G06T 7/80 - Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06T 7/70 - Determining position or orientation of objects or cameras
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit

Abstract

System and methods for causing movement of an autonomous vehicle. The methods comprise: determining candidate trajectories of the autonomous vehicle using candidate actions forecasted for a virtual doppelganger and reactive actions predicted for a non-Ego actor that is located in proximity to the autonomous vehicle; selecting first candidate trajectories of the candidate trajectories in which the autonomous vehicle has an influence on a response of the non-Ego actor; using the first candidate trajectories for the autonomous vehicle to refine the reactive actions that were predicted for the non-Ego actor; using the reactive actions that were refined to select one of the first candidate trajectories as a selected trajectory for the autonomous vehicle to follow; and causing an automation subsystem of the autonomous vehicle to move the autonomous vehicle along the selected trajectory.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots

Abstract

Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle and identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs. The methods also include selecting a subset of the plurality of unlabeled sensor data logs that have an importance score greater than a threshold, the importance score being determined based on the one or more trends. The subset of the plurality of unlabeled sensor data logs is used for updating the machine learning model to generate an updated model.

IPC Classes  ?

• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06F 18/2115 - Selection of the most significant subset of features by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 20/00 - Machine learning
• G06V 10/20 - Image preprocessing
• G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
• G06V 20/64 - Three-dimensional objects

Abstract

Devices, systems, and methods are provided for testing and validation of a cleaning system for a sensor such as a camera. A device may capture a first image of a target using the sensor, wherein the sensor is in a clean state, and wherein the target is in a line of sight of the sensor. The device may apply an obstruction to a portion of a lens of the sensor. The device may apply a cleaning system to the lens. The device may capture a post-clean image after applying the cleaning system. The device may determine a post-clean image quality score based on comparing the post clean image to the first image. The device may compare the post-clean image quality score to a validation threshold. The device may determine a validation state of the cleaning system based on the comparison.

IPC Classes  ?

• H04N 17/00 - Diagnosis, testing or measuring for television systems or their details
• G06F 18/22 - Matching criteria, e.g. proximity measures
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]

Abstract

A mounting device includes an elongated beam having a first end portion, a second end portion, and a side surface extending between the first end portion and the second end portion. The mounting device also includes a first camera mount attached to the first end portion configured to support a first camera, a second camera mount attached to the second end portion configured to support a second camera, and a bracket for fixedly connecting the elongated beam to a vehicle. The bracket is positioned between the first end portion and the second end portion. The bracket includes at least one base configured to be attached to the vehicle and a wall extending from the at least one base comprising an opening sized to receive the elongated beam, such that engagement between the wall and the elongated beam restricts rotation of the elongated beam about multiple axes.

IPC Classes  ?

• B60R 11/04 - Mounting of cameras operative during driveArrangement of controls thereof relative to the vehicle
• B60R 11/00 - Arrangements for holding or mounting articles, not otherwise provided for
• G03B 17/56 - Accessories
• G03B 35/08 - Stereoscopic photography by simultaneous recording

Abstract

Systems, methods, and computer-readable media are disclosed for obtaining data from multiple internal vehicle networks. An example method may include receiving, using a first internal communication network of a vehicle, a first query for data from one or more devices of the vehicle, wherein the first query is formatted for communication over the first internal communication network. The example method may also include generating, based on the first query, a second query associated with a supplemental communication network, wherein the supplemental communication network is also internal to the vehicle. The example method may also include sending, using the supplemental communication network, the second query to a first device, the first device being located on the supplemental communication network. The example method may also include receiving, from the first device, data relating to the second query. The example method may also include sending, using the first internal communication network of the vehicle, the data relating to the second query.

IPC Classes  ?

• H04L 12/40 - Bus networks

Abstract

Provided are autonomous vehicles and methods of controlling autonomous vehicles through topological planning with bounds, including receiving map data and sensor data, expanding a topological tree by adding a plurality of nodes to represent a plurality of actions associated with the plurality of constraints, generating a bound based on a constraint in the geographic area, the bound associated with an action for navigating the autonomous vehicle relative to the at least one constraint, storing the bound in a central bound storage, linking a set of bounds of a tree node to the bound via a bound identifier, wherein the first bound is initially linked as an active bound, or alternatively, as an inactive bound after determining it is not the most restrictive bound at any sample index, and control the autonomous vehicle based on the topological tree, to navigate the plurality of constraints.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/34 - Route searchingRoute guidance

Abstract

A mounting device for selectively positioning an optical element within a field-of-view of an optical sensor of a vehicle includes: a housing defining an opening sized to fit over an aperture of the optical sensor; a holder for the optical element connected to the housing and positioned such that, when the holder is in a first position, the optical element is at least partially within the field-of-view of the optical sensor; and a motorized actuator. The motorized actuator can be configured to move the holder to adjust the position of the optical element relative to the field-of-view of the optical sensor.

IPC Classes  ?

• H04N 17/00 - Diagnosis, testing or measuring for television systems or their details
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01S 7/497 - Means for monitoring or calibrating
• G06V 10/88 - Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters
• G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• H04N 23/51 - Housings
• H04N 23/54 - Mounting of pick-up tubes, electronic image sensors, deviation or focusing coils
• H04N 23/55 - Optical parts specially adapted for electronic image sensorsMounting thereof
• H04N 23/57 - Mechanical or electrical details of cameras or camera modules specially adapted for being embedded in other devices

Abstract

Systems, methods, and computer-readable media for receiving, by a smart infrastructure device and from a first vehicle, first information associated with the first vehicle in a first format associated with a first communication protocol. The first information is converted from the first format into an agnostic format. An image, video, or real-time feed of an environment of the smart infrastructure device is captured. The first vehicle and a second vehicle in the image, video, or real-time feed is identified. It is determined that the second vehicle is temporarily or permanently incapable of performing a communication with the smart infrastructure device based on the image, video, or real-time feed. The image, video, or real-time feed is analyzed to generate second information associated with the second vehicle. The second information is converted into the agnostic format.

IPC Classes  ?

• G01C 21/34 - Route searchingRoute guidance
• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G01C 21/36 - Input/output arrangements for on-board computers
• H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
• H04W 4/46 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]

Abstract

Systems for managing access to an autonomous vehicle includes an autonomous vehicle including a plurality of storage compartments, wherein each of the plurality of storage compartments comprises a locking mechanism and at least one processor to receive data associated with an item to be positioned in a storage compartment of the plurality of storage compartments, determine that one of the plurality of storage compartments has storage capacity for the item, designate one of the plurality of storage compartments for storage of the item, activate the locking mechanism of the designated storage compartment to lock the designated storage compartment after the item is positioned in the designated storage compartment, and activate the locking mechanism of the designated storage compartment to unlock the designated storage compartment to allow removal of the item from the designated storage compartment. Methods, computer program products, and autonomous vehicles are also disclosed.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60R 25/24 - Means to switch the anti-theft system on or off using electronic identifiers containing a code not memorised by the user
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

Methods, systems, and products for generating an updated map for use with an autonomous vehicle driving operation or a simulation thereof may include obtaining first map data associated with a first map of a geographic location including a roadway, and the first map data may include at least one first lane segment. Second map data associated with a second map of the geographic location may be obtained, and the second map data may include at least one second lane segment. A plurality of non-overlapping areas may be determined based on the first lane segment(s) and the second lane segment(s). A first non-overlapping and/or a first warp point within the first non-overlapping area may be selected. The first lane segment(s) may be warped around the first warp point to increase a total overlapping area based on the based on the second lane segment(s) and the first lane segment(s) after warping.

IPC Classes  ?

• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G05D 1/02 - Control of position or course in two dimensions

Abstract

Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

IPC Classes  ?

• G06T 7/00 - Image analysis
• B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
• B60W 30/095 - Predicting travel path or likelihood of collision
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06N 3/045 - Combinations of networks
• G06N 3/088 - Non-supervised learning, e.g. competitive learning
• G06T 7/50 - Depth or shape recovery
• G06T 7/60 - Analysis of geometric attributes
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
• G06V 10/20 - Image preprocessing
• G06V 10/24 - Aligning, centring, orientation detection or correction of the image
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
• G06V 20/64 - Three-dimensional objects

Abstract

Disclosed herein are systems, methods, and computer program products for controlling data collection by resources. The methods comprise: receiving real-world data collected by the resources in accordance with data collection mission (DCM) parameters; receiving user defined DCM goal(s); updating goal(s) for DCM mission(s) based on the real-world data and the user defined DCM goal(s); modifying the data DCM parameter(s) based on the updated goal(s) and which ones of the resources are still available for DCMs; and causing data collection operations (which are currently being performed by the resource(s)) to change in accordance with the modified DCM parameter(s).

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 50/06 - Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot
• G06F 16/26 - Visual data miningBrowsing structured data
• G06N 20/00 - Machine learning

Abstract

Disclosed herein are systems, methods, and computer program products for generating and using map information. For example, the method includes: identifying data collection mission area(s) (DCMAs) within a geographic location that is to be covered by robotic device(s) during a data collection mission (DCM); generating a route to be traversed by robotic device(s) in DCMAs (the route being configured to cause robotic device(s) to traverse each two-way road at least one time in two opposing directions); causing robotic device(s) to perform DCM by following the route and collecting data; causing robotic device(s) to discontinue collecting data in response to a trigger event; and using the data collected during DCM to generate or update the map information. The map information may be used to facilitate controlled movement of a vehicle.

IPC Classes  ?

• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures

Abstract

A method of transferring data between an autonomous vehicle and a vehicle traffic control infrastructure system. The method includes receiving, by a communication device of a vehicle, a data payload for a smart traffic control infrastructure node. The method includes, by a computing device of the vehicle: determining that the vehicle is or will be within a communication range of the smart traffic control infrastructure node, determining a length of time that the vehicle will be in the communication range of the smart traffic control infrastructure node, and assembling a communication package with at least a portion of the data payload that can be transferred in the determined length of time. The method includes, by a communication device of the vehicle when the vehicle has an ad hoc communication link with the smart traffic control infrastructure node, transmitting the assembled communication package to the smart node.

IPC Classes  ?

• G08G 1/01 - Detecting movement of traffic to be counted or controlled
• G06F 8/65 - Updates
• H04W 84/18 - Self-organising networks, e.g. ad hoc networks or sensor networks
• G06N 20/00 - Machine learning
• H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting

Abstract

A mounting device includes an elongated beam having a first end portion, a second end portion, and a side surface extending between the first end portion and the second end portion. The mounting device also includes a first camera mount attached to the first end portion configured to support a first camera, a second camera mount attached to the second end portion configured to support a second camera, and a bracket for fixedly connecting the elongated beam to a vehicle. The bracket is positioned between the first end portion and the second end portion. The bracket includes at least one base configured to be attached to the vehicle and a wall extending from the at least one base comprising an opening sized to receive the elongated beam, such that engagement between the wall and the elongated beam restricts rotation of the elongated beam about multiple axes.

IPC Classes  ?

• B60R 11/04 - Mounting of cameras operative during driveArrangement of controls thereof relative to the vehicle
• G03B 35/08 - Stereoscopic photography by simultaneous recording
• G03B 17/56 - Accessories
• B60R 11/00 - Arrangements for holding or mounting articles, not otherwise provided for

Abstract

This document describes methods by which a system determines a pickup/drop-off zone (PDZ) to which a vehicle will navigate to perform a ride service request. The system will access map data that includes the destination interval and define a geometric interval at the requested destination of the ride service request by: (i) using the vehicle's length and the road's speed limit at the destination to calculate a minimum allowable length for the interval; and (ii) setting start point and end point boundaries for the interval having an intervening distance that is equal to or greater than the minimum allowable length. The system will then generate a path to guide the vehicle to the interval.

IPC Classes  ?

• G01C 21/36 - Input/output arrangements for on-board computers
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G08G 1/14 - Traffic control systems for road vehicles indicating individual free spaces in parking areas

Abstract

Devices, systems, and methods are provided for mitigating vehicle power loss. A vehicle charging system may include a power supply, and a voltage control device associated with receiving first voltage from the power supply, providing the first voltage to a hybrid vehicle or a battery electric vehicle, and blocking a second voltage from the hybrid vehicle or the battery electric vehicle, wherein the vehicle charging system is external to the hybrid vehicle or the battery electric vehicle.

IPC Classes  ?

• H02J 7/00 - Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
• B60L 58/10 - Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
• H02J 7/14 - Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from dynamo-electric generators driven at varying speed, e.g. on vehicle

Abstract

Devices, systems, and methods are provided for radar elevation angle measurement. A radar elevation angle measurement system may transmit one or more signals from a radar towards a reflection structure comprised of individually controlled motors operable to rotate one or more corner reflectors. The radar elevation angle measurement system may receive echo signals at the radar from each of the one or more corner reflectors that sequentially transitioned to an ON position based on each of the one or more corner reflectors being sequentially rotated to be in an ON then an OFF positions. The radar elevation angle measurement system may collect data associated with the echo signals received from the one or more corner reflectors. The device may identify peak signal values based on the collected data. The radar elevation angle measurement system may calculate a radar pitch angle of the radar based on the peak signal values.

IPC Classes  ?

• G01S 7/40 - Means for monitoring or calibrating
• G01S 13/08 - Systems for measuring distance only
• G01S 13/931 - Radar or analogous systems, specially adapted for specific applications for anti-collision purposes of land vehicles

Abstract

Disclosed herein are system and method embodiments to implement a validation of a vector map. The validation process may merge proposed and persisted high-definition mapping data, evaluate the high-definition mapping data with a set of customizable validation rules, return/persist validation results, and provide a means to acknowledge validation failures to minimize creation of problematic vector map content.

IPC Classes  ?

• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles

Abstract

Disclosed herein are system, method, and computer program product embodiments for generating and refining simulation scenarios. For example, the method includes generating multiple base scenarios, each including one or more constant and one or more variable parameters. For each of the base scenarios, the method includes generating scenario variations, each of which is associated with a unique combination of values assigned to its base scenario’s parameters. The method further includes determining a system boundary in a parameter space defined by the variable parameters, wherein the system boundary divides the parameter space into a region including successful scenario variations and a region including unsuccessful scenario variations, and generating additional scenario variations within a threshold distance of the system boundary. The method further includes simulating operation of an autonomous vehicle (AV) using one or more generated scenario variations.

IPC Classes  ?

• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G05D 1/02 - Control of position or course in two dimensions
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots

Abstract

Systems and methods for operating a robot. The methods comprise: performing, by a processor, operations to detect an object that is moving; identifying, by the processor, detected behavior of the object that constitutes an unrecognized behavior; predicting, by the processor, future movement of the object based on a circle having a radius that is function of a velocity of the object; and controlling operations of the robot based on the predicting.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
• B60W 30/095 - Predicting travel path or likelihood of collision

Abstract

Disclosed herein are system, method, and computer program product embodiments for automated autonomous vehicle pose validation. An embodiment operates by generating a range image from a point cloud solution comprising a pose estimate for an autonomous vehicle. The embodiment queries the range image for predicted ranges and predicted class labels corresponding to lidar beams projected into the range image. The embodiment generates a vector of features from the range image. The embodiment compares a plurality of values to the vector of features using a binary classifier. The embodiment validates the autonomous vehicle pose based on the comparison of the plurality of values to the vector of features using the binary classifier.

IPC Classes  ?

• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• G01S 7/48 - Details of systems according to groups , , of systems according to group
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods

Abstract

Devices, systems, and methods are provided for enhanced pointing angle validation. A device may generate a collimated beam using a light source emitting a light beam through a fiducial target in an optical instrument. The device may capture an image fiducial target using a camera, wherein the camera is mounted on a mounting datum that is orthogonal to the collimated beam. The device may determine a pointing angle associated with camera based on the captured image of the fiducial target. The device may compare a location of the fiducial target in the image to an optical center of the camera. The device may determine a validation status of camera based on the location of the fiducial target in the image.

IPC Classes  ?

• H04N 23/57 - Mechanical or electrical details of cameras or camera modules specially adapted for being embedded in other devices
• B60R 1/12 - Mirror assemblies combined with other articles, e.g. clocks
• G02B 27/30 - Collimators

Abstract

A system and method for performing visual odometry is disclosed. In aspects, the system implements methods to generate an image pyramid based on an input image received. A refined pose prior information representing a location and orientation of the autonomous vehicle can be generated based on one or more images of the image pyramid. One or more seed points can be selected from the one or more images of the image pyramid. One or more refined seed representing the one or more seed points with added depth values can be generated. One or more scene points can be generated based on the one or more refined seed points. A point cloud can be generated based on the one or more scene points.

IPC Classes  ?

• G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
• B60W 40/02 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to ambient conditions
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06T 1/20 - Processor architecturesProcessor configuration, e.g. pipelining
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06T 7/11 - Region-based segmentation
• G06T 7/55 - Depth or shape recovery from multiple images
• G06T 9/00 - Image coding
• G01S 19/48 - Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system

Abstract

Methods and systems for determining whether a camera of an autonomous vehicle (AV) is calibrated are disclosed. The method includes using image frames captured by a camera of the AV and LIDAR point clouds captured by a LIDAR system of the AV to calculate an AV pose calibration metric. The method also includes: measuring a distance metric between a three-dimensional bounding box around an object and a two-dimensional bounding box in an image captured by the camera; using the AV pose calibration metric and the distance calibration metric to generate a confidence score; and in response to the confidence score being below a threshold, generating a signal indicating that the camera is not calibrated.

IPC Classes  ?

• G06T 7/80 - Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

Systems and methods for mutual discovery in autonomous rideshare between passengers and vehicles may receive a pick-up request to pick-up a user with an autonomous vehicle and interact with the user to perform an operation associated with the autonomous vehicle and/or update a user profile associated with the user.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G05D 1/02 - Control of position or course in two dimensions
• B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling

Abstract

Methods, systems, and products for parallax estimation for sensors for autonomous vehicles may include generating a two-dimensional grid based on a field of view of a first sensor. For each respective point in the grid, a three-dimensional position of an intersection point between a first ray from the first sensor and a second ray from a second sensor may be determined. For each respective intersection point, a respective solid angle may be determined based on a first three-dimensional vector from the first sensor and a second three-dimensional vector from the second sensor to the intersection point. A matrix may be generated based on a distance from the first sensor, a distance from the second sensor, and the solid angle for each respective intersection point. At least one metric may be extracted from the matrix. An arrangement of the first and second sensors may be adjusted based on the metric(s).

IPC Classes  ?

• G06T 7/80 - Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
• B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01S 7/497 - Means for monitoring or calibrating
• H04N 17/00 - Diagnosis, testing or measuring for television systems or their details
• H04N 23/695 - Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects

Abstract

Disclosed herein are system, method, and computer program product embodiments for switching between local and remote guidance instructions for autonomous vehicles. For example, the method includes, in response to monitoring one or more actions of objects detected in a scene in which the autonomous robotic system is moving, causing the autonomous robotic system to slow or cease movement in the scene. The method includes detecting a trigger condition based on movement of the autonomous robotic system in the scene. In response to the one or more monitored actions and detecting the trigger condition, the method includes transmitting a remote guidance request to a remote server. After transmitting the remote guidance request, the method includes receiving remote guidance instructions from the remote server and causing the autonomous robotic system to begin operating according to the remote guidance instructions.

IPC Classes  ?

• G05D 1/02 - Control of position or course in two dimensions
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles

Abstract

Disclosed herein are system and method embodiments to implement a validation of an SfM map. An embodiment operates by receiving a motion-generated map corresponding to a digital image, generating a first depth map, wherein the first depth map comprises depth information for one or more triangulated points located within the motion generated image. The embodiment further receives a light detection and ranging (lidar) generated point cloud including at least a portion of the one or more triangulated points, splats the lidar point cloud proximate to the portion of the one or more triangulated points and generates a second depth map for the portion and identifies an incorrect triangulated point, of the one or more triangulated points, based on comparing the first depth information to the second depth information. The incorrect triangulated points may be removed from the SfM map or marked with a low degree of confidence.

IPC Classes  ?

• G06T 7/579 - Depth or shape recovery from multiple images from motion
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles

Abstract

This document discloses system, method, and computer program product embodiments for detecting an object. For example, the method includes generating a plurality of cuboids by performing the following operations: defining a plurality of first cuboids each encompassing lidar data points that are plotted on a respective 3D graph of a plurality of 3D graphs; accumulating the lidar data points encompassed by the plurality of first cuboids; computing an extent using the accumulated lidar data points; and defining a second cuboid that has dimensions specified by the extent. The first cuboids and/or the second cuboid may be used to detect the object.

IPC Classes  ?

• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
• G06T 7/70 - Determining position or orientation of objects or cameras
• G01S 17/894 - 3D imaging with simultaneous measurement of time-of-flight at a 2D array of receiver pixels, e.g. time-of-flight cameras or flash lidar

Abstract

Systems may include a processor to, in response to determining at least one segment of a field of view (FOV) of a first sensor of an autonomous vehicle that overlaps with a FOV of at least one second sensor of the autonomous vehicle, calculate a scaling factor for diagnostic coverage for the at least one segment based on a value of modality overlap (MoD) for the at least one segment, calculate, based on the scaling factor, a value of a metric of hardware failure for the first sensor, and compare the value of the metric of hardware failure to a threshold value to determine whether to increase a diagnostic coverage of the first sensor. Methods, computer program products, and autonomous vehicles are also disclosed.

IPC Classes  ?

• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles

Abstract

Systems and methods for managing permissions and authorizing access to a service supported by a computing device. The methods comprise by a computing device: intercepting a request to access the service sent along with a certificate including a first tenant identifier (the first tenant identifier identifying a first business entity other than a second business entity providing the service); using the first tenant identifier to obtain permission information from a datastore (the permission information specifying which resources of a plurality of resources can be returned in response to requests from the first business entity); generating a web authentication token including the first tenant identifier and the permission information; and initiating operations of the service in response to a validation of the web authentication token.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G01C 21/36 - Input/output arrangements for on-board computers
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 10/083 - Shipping
• G08G 1/00 - Traffic control systems for road vehicles
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system

Abstract

Methods and systems for estimating a time of arrival for a vehicle at a destination are disclosed. The system will access an adjacency graph comprising nodes and edges. Each node is associated with a unique location in a geographic area in which the vehicle is traveling. Each edge connects two of the nodes and is associated with an estimated travel time between the two connected nodes. The system will select, from the locations in adjacency graph, a first location that is near the vehicle and a second location that is near the destination. The location and destination are each associated with nodes in adjacency graph. The system will calculate a shortest path along the edges in the adjacency graph from the location and destination nodes, and it will calculate an estimated time of arrival for the vehicle as a function of the estimated travel times along the shortest path.

IPC Classes  ?

• G06Q 10/00 - AdministrationManagement
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G01C 21/36 - Input/output arrangements for on-board computers
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 10/083 - Shipping
• G08G 1/00 - Traffic control systems for road vehicles
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/40 - Network security protocols

Abstract

Methods and systems that use a phantom vehicle to help generate a planned path for a real-world vehicle are described. The system will identify a starting point and a destination for a trip of the real-world vehicle. The system will select, from the data store of vehicle profiles, a phantom vehicle having an associated motion planning system that corresponds to a system that is deployed on the real-world vehicle. The system will use a high definition map to generate a planned route for the real-world vehicle from the starting point to the destination in the map. The system will run a simulation in which the phantom vehicle moves along the planned route in the map. The system will then output a record of the simulation to a user of the real world-vehicle or to a system of the real-world vehicle.

IPC Classes  ?

• G06Q 10/083 - Shipping
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G01C 21/36 - Input/output arrangements for on-board computers
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G08G 1/00 - Traffic control systems for road vehicles
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/40 - Network security protocols

Abstract

Methods and systems for obtaining serviceable areas for a robotic system in a metropolitan area are described. A computing device obtains information about places where (i) the system can route to and from in the area and/or (ii) the system can stop in the area. The computing device uses the information to generate clusters of places where the robotic system can route or stop in the metropolitan area. The computing device creates a geometric shape for each cluster, wherein each shape which has a boundary defined by outermost places contained in the cluster. The computing device uses the geometric shapes to define the serviceable areas for the robotic system within the metropolitan area. The computing device uses the serviceable areas to generate a map displaying at least one geographic area representing a portion of the metropolitan area where a concentrated number of the places exist.

IPC Classes  ?

• G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

Methods and systems for generating a planned path for a vehicle are disclosed. Upon receiving a trip service request, a processor will access a data store containing multiple candidate motion planning systems, each of which is associated with at least one vehicle or fleet. The processor will identify a starting point and a destination for the trip service request, and it will use an identifier for the vehicle or its fleet to select, from the candidate systems, a motion planning system. The processor will use the functions of the selected motion planning system to generate candidate trajectories for the first vehicle from the starting point to the destination in a high definition map. The processor will select a planned route from the candidate trajectories, and it will output trip instructions to cause the vehicle to move along the planned route.

IPC Classes  ?

• G06Q 10/00 - AdministrationManagement
• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• G01C 21/36 - Input/output arrangements for on-board computers
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 10/083 - Shipping
• G08G 1/00 - Traffic control systems for road vehicles
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/40 - Network security protocols

Abstract

A system and method for performing visual localization is disclosed. In aspects, the system implements methods to generate a global point cloud, the global point cloud representing a plurality of point clouds. The global point cloud can be mapped to a prior map information to locate a position of an autonomous vehicle, the prior map information representing pre-built geographic maps. The position of the autonomous vehicle can be estimated based on applying sensor information obtained from sensors and software of the autonomous vehicle to the mapped global point cloud.

IPC Classes  ?

• G06T 7/70 - Determining position or orientation of objects or cameras
• G01C 21/30 - Map- or contour-matching
• G06T 7/50 - Depth or shape recovery

Abstract

Systems, methods, and autonomous vehicles may obtain one or more images associated with an environment surrounding an autonomous vehicle; determine, based on the one or more images, an orientation of a head worn item of protective equipment of an operator of a vehicle; determine, based on the orientation of the head worn item of protective equipment, a direction of a gaze of the operator and a time period associated with the direction of the gaze of the operator; determine, based on the direction of the gaze of the operator and the time period associated with the direction of the gaze of the operator, a predicted motion path of the vehicle; and control, based on the predicted motion path of the vehicle, at least one autonomous driving operation of the autonomous vehicle.

IPC Classes  ?

• B60W 30/095 - Predicting travel path or likelihood of collision
• H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/20 - Movements or behaviour, e.g. gesture recognition
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• G06F 18/22 - Matching criteria, e.g. proximity measures

Abstract

This document describes methods by which a system determines a pickup/drop-off zone (PDZ) to which a vehicle will navigate to perform a ride service request. The system will define a PDZ that is a geometric interval that is within a lane of a road at the requested destination of the ride service request by: (i) accessing map data that includes the geometric interval; (ii) using the vehicle's length and the road's speed limit at the destination to calculate a minimum allowable length for the PDZ; (iii) setting, start point and end point boundaries for the PDZ having an intervening distance that is equal to or greater than the minimum allowable length; and (iv) positioning the PDZ in the lane at or within a threshold distance from the requested destination. The system will then generate a path to guide the vehicle to the PDZ.

IPC Classes  ?

• G01C 21/36 - Input/output arrangements for on-board computers
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G08G 1/14 - Traffic control systems for road vehicles indicating individual free spaces in parking areas

Abstract

A fleet management system will receive, from each vehicle of a fleet of vehicles, a vehicle identification number (VIN). The system also will receive data elements that comprise: (a) vehicle operational parameters gathered during a run of the vehicle; (b) a hardware identification code that identifies a hardware component of the vehicle; and/or (c) a software identification code that identifies an installed software component in the vehicle. The processor will generate a data block that comprises the VIN and the one or more data elements. The processor will then save the data block to a shared digital ledger that includes VINs and data elements for a plurality of the vehicles in the fleet.

IPC Classes  ?

• G08G 1/00 - Traffic control systems for road vehicles
• G06F 8/65 - Updates
• G07C 5/00 - Registering or indicating the working of vehicles
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols

Abstract

A system for communicating with multiple vehicles or other electronic devices that share a common media access control (MAC) or other address is disclosed. Upon receiving a certificate signing request (CSR) from a connected device and determining that the device does not have a unique address, the system will generate a unique address for the device and embedding the unique addresses in a certificate, sign the certificate, and transfer the certificate to the device. Then, when the system communicates with the device, the system may use that unique address to identify the device.

IPC Classes  ?

• G06F 7/04 - Identity comparison, i.e. for like or unlike values
• H04L 9/40 - Network security protocols
• G07C 5/00 - Registering or indicating the working of vehicles

Abstract

Disclosed herein are systems, methods, and computer program products for predicting movement of an object in a real-world environment. The methods comprise: obtaining a plurality of image frames captured in a sequence during a period of time; identifying first image frames of the plurality of image frames that contain an image of at least one object with one or more turn signals; analyzing the first image frames to obtain a classification for a pose of the at least one object; using the classification of the pose of the at least one object to further obtain a type classification for at least one of the turn signals and a state classification for a state of at least one of the turn signals; and predicting movement of the at least one object based at least on the type and state classifications obtained for at least one of the turn signals.

IPC Classes  ?

• G06T 11/20 - Drawing from basic elements, e.g. lines or circles
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06T 7/20 - Analysis of motion
• G06T 7/00 - Image analysis
• G06T 7/90 - Determination of colour characteristics
• G05D 1/02 - Control of position or course in two dimensions
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
• G06V 20/40 - ScenesScene-specific elements in video content
• G06F 18/24 - Classification techniques
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries

Abstract

Provided are systems, methods, and computer program products for secure code signing of software artifacts in a permissioned blockchain for recording, distributing, and auditing of software artifacts via hash digests, artifact signatures, and worker signatures, comprising a build system worker to generate a software release blockchain, and insert software artifact blocks including a hash digest of the software artifact, a code signing worker to generate and insert a signature block of a software artifact block comprising a signature of the hash digest in the software artifact block, a check system worker to generate a check block associated with the signature block, the check block an attestation verifying at least the integrity of the hash digest and the authenticity of the signature of the hash digest, and one or more processors, configured to verify the software release blockchain based at least on the check block.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 8/71 - Version control Configuration management

Abstract

Methods and systems for detecting objects near an autonomous vehicle (AV) are disclosed. An AV will capture an image. A trained network will process the image at a lower resolution and generate a first feature map that classifies object(s) within the image. The system will crop the image and use the network to process the cropped section at a higher resolution to generate a second feature map that classifies object(s) that appear within the cropped section. The system will crop the first feature map to match a corresponding region of the cropped section of the image. The system will fuse the cropped first and second feature maps to generate a third feature map. The system may output the object classifications in the third feature map to an AV system, such as a motion planning system that will use the object classifications to plan a trajectory for the AV.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06F 18/213 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 3/02 - Neural networks
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

Systems, methods, and computer-readable media are disclosed for a systems and methods for improved smart infrastructure data transfer. An example method may involve identifying that a software update is available for a smart infrastructure system. The example method may also involve determining, by a processor of the smart infrastructure system and using a signal strength between a first vehicle and the smart infrastructure system, that the first vehicle is within a threshold range of the smart infrastructure system. The example method may also involve establishing, by the smart infrastructure system, a first ad-hoc peer-to-peer communication link with the first vehicle. The example method may also involve sending, to the vehicle, a request for the software update. The example method may also involve receiving, from the vehicle, at least a first portion of the software update that is transferred using the first ad-hoc peer-to-peer communication link.

IPC Classes  ?

• G06F 8/65 - Updates
• H04L 67/00 - Network arrangements or protocols for supporting network services or applications
• H04L 67/104 - Peer-to-peer [P2P] networks

Abstract

Systems and methods for operating an autonomous vehicle (AV) are provided. The method includes detecting one or more objects in an environment, predicting a first set of predicted object trajectories comprising one or more trajectories for each of the detected one or more objects, generating a plurality of candidate AV trajectories for the AV, scoring each of the candidate AV trajectories according to a cost function, using the scoring to select a final AV trajectory for execution, determining which of the predicted object trajectories affected the final AV trajectory and which did not do so, adding the predicted object trajectories that affected the final AV trajectory to a persisted prediction cache, excluding from the persisted prediction cache any predicted object trajectories that did not affect the final AV trajectory, and executing the final AV trajectory to cause the AV to move along the final AV trajectory.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles

Abstract

Devices, methods, and systems may obtain at least one point cloud, segment points in the at least one point cloud into a plurality of segments, train a neural network using known segments and a first loss function to generate a first trained neural network, train the first trained neural network using outlier segments and a second loss function to generate a second trained neural network, and train an extended isolation forest by applying an extended isolation algorithm to features of the known segments and features of the outlier segments to generate an anomaly score for each segment.

IPC Classes  ?

• G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
• B60W 50/06 - Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01S 7/481 - Constructional features, e.g. arrangements of optical elements
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 18/23 - Clustering techniques
• G06N 3/08 - Learning methods
• G06T 7/11 - Region-based segmentation
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

Methods, systems, and computer program products for navigating a vehicle are disclosed. The methods include extracting lane segment data associated with lane segments of a vector map that are within a region of interest, and analyzing the lane segment data and a heading of the vehicle to determine whether motion of the vehicle satisfies a condition. The condition can be associated with (i) an association between the heading of the vehicle and a direction of travel of a lane that corresponds to the current location of the vehicle and/or (ii) a minimum stopping distance to an imminent traffic control measure in the lane that corresponds to the current location of the vehicle. When the motion does not satisfy the condition, the methods include causing the vehicle to perform a motion correction.

IPC Classes  ?

• B60W 50/00 - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
• B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
• G08G 1/01 - Detecting movement of traffic to be counted or controlled
• G08G 1/097 - Supervising of traffic control systems, e.g. by giving an alarm if two crossing streets have green light simultaneously
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Abstract

Systems providing a comparison of results of simulations of operation of a simulated autonomous vehicle may include a processor to: perform a first simulation of operation of a simulated autonomous vehicle based on first autonomous vehicle control code, receive second autonomous vehicle control code that includes a second version of software code associated with controlling operations of the simulated autonomous vehicle, the second version including a modification of a first version of software code associated with controlling operations of the simulated autonomous vehicle, perform a second simulation of operation of the simulated autonomous vehicle based on the second autonomous vehicle control code, and display an indication that second values of one or more metrics that resulted from the second simulation are different from one or more first values of the one or more metrics that resulted from the first simulation. Methods, computer program products, and autonomous vehicles are also disclosed.

IPC Classes  ?

• G06F 30/20 - Design optimisation, verification or simulation
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• B60W 30/095 - Predicting travel path or likelihood of collision
• B60W 40/10 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to vehicle motion
• G06F 30/15 - Vehicle, aircraft or watercraft design

Abstract

This document discloses system, method, and computer program product embodiments for operating an autonomous vehicle (AV). For example, the method includes performing the following operations by a muxing tool when AV is deployed within a particular geographic area in a real-world environment: receiving perception data that is representative of at least one actual object which is perceived while AV is deployed within the particular geographic area in a real-world environment; receiving simulation data that represents a simulated object that could be perceived by AV in the real-world environment and that was generated using a simulation scenario which is selected from a plurality of simulation scenarios based on at least one of the particular geographic area in which AV is currently located and a current operational state of AV; and generating augmented perception data by combining the simulation data with the perception data.

IPC Classes  ?

• B60W 30/095 - Predicting travel path or likelihood of collision
• B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
• G01S 19/42 - Determining position
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/34 - Route searchingRoute guidance
• B60W 40/105 - Speed
• G01C 21/00 - NavigationNavigational instruments not provided for in groups

Abstract

Methods and systems for controlling navigation of a vehicle are disclosed. The system will first identify a plurality of goal points corresponding to a drivable area that a vehicle is traversing or will traverse, where the plurality of goal points are potential targets that an uncertain road user (URU) within the drivable area can use to exit the drivable area. The system will then receive perception information relating to the URU within the drivable area, and identify a target exit point from the plurality goal points based on a score. The score is computed based on the received perception information and a loss function. The system will generate a trajectory of the URU from a current position of the URU to the target exit point, and control navigation of the vehicle to avoid collision with the URU.

IPC Classes  ?

• B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
• B60W 30/095 - Predicting travel path or likelihood of collision
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles

Abstract

Methods and systems for controlling navigation of a vehicle are disclosed. The system will first detect a URU within a threshold distance of a drivable area that a vehicle is traversing or will traverse. The system will then receive perception information relating to the URU, and use a plurality of features associated with each of a plurality of entry points on a drivable area boundary that the URU can use to enter the drivable area to determine a likelihood that the URU will enter the drivable area from that entry point. The system will then generate a trajectory of the URU using the plurality of entry points and the corresponding likelihoods, and control navigation of the vehicle while traversing the drivable area to avoid collision with the URU.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles

Abstract

Method and systems for generating vehicle motion planning model simulation scenarios are disclosed. The system receives a base simulation scenario with features of a scene through which a vehicle may travel. The system then generates an augmentation element with a simulated behavior for an object in the scene by: (i) accessing a data store in which behavior probabilities are mapped to object types to retrieve a set of behavior probabilities for the object; and (ii) applying a randomization function to the behavior probabilities to select the simulated behavior for the object. The system will add the augmentation element to the base simulation scenario at the interaction zone to yield an augmented simulation scenario. The system will then apply the augmented simulation scenario to an autonomous vehicle motion planning model to train the motion planning model.

IPC Classes  ?

• B60W 30/095 - Predicting travel path or likelihood of collision
• B60W 30/18 - Propelling the vehicle
• G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots

Abstract

Systems, methods, and computer-readable media are disclosed for identifying light flares in images. An example method may involve receiving an image from an imaging device, the image including data indicative of a flare artifact originating from a region of the image. The example method may also involve determining, based on the image data, a first array of pixels extending radially outwards from the region and a second array of pixels extending radially outwards from the region. The example method may also involve creating, based on the image data, a flare array, the flare array including the first array of pixels and the second array of pixels. The example method may also involve determining, based on the flare array, a peak flare artifact value indicative of a size of the flare artifact; and determining, based on the peak flare artifact value, a flare artifact score for the imaging device.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06T 3/60 - Rotation of whole images or parts thereof
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06T 11/60 - Editing figures and textCombining figures or text

Abstract

A mounting device for selectively positioning an optical element within a field-of-view of an optical sensor of a vehicle includes: a housing defining an opening sized to fit over an aperture of the optical sensor; a holder for the optical element connected to the housing and positioned such that, when the holder is in a first position, the optical element is at least partially within the field-of-view of the optical sensor; and a motorized actuator. The motorized actuator can be configured to move the holder to adjust the position of the optical element relative to the field-of-view of the optical sensor.

IPC Classes  ?

• H04N 17/00 - Diagnosis, testing or measuring for television systems or their details
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
• G06V 10/88 - Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
• H04N 23/51 - Housings
• H04N 23/54 - Mounting of pick-up tubes, electronic image sensors, deviation or focusing coils
• H04N 23/57 - Mechanical or electrical details of cameras or camera modules specially adapted for being embedded in other devices
• H04N 23/55 - Optical parts specially adapted for electronic image sensorsMounting thereof
• G01S 7/497 - Means for monitoring or calibrating

Abstract

Devices, systems, and methods are provided for enhanced sensor cleaning validation. A device may receive a signal from a sensor indicative of an obstruction on the sensor. The device may activate a cleaning system at a degree of actuation responsive to the obstruction. The device may then obtain a first post-clean performance measurement of the sensor. The device may then adjust the degree of actuation of the cleaning system based on a degradation measurement between a baseline performance measurement associated with a clean performance baseline of the sensor and the first post-clean performance measurement.

IPC Classes  ?

• B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
• B60Q 11/00 - Arrangement of monitoring devices for devices provided for in groups
• B60S 1/46 - Cleaning windscreens, windows, or optical devices using liquidWindscreen washers
• B60S 1/54 - Cleaning windscreens, windows, or optical devices using gas, e.g. hot air
• B60W 50/04 - Monitoring the functioning of the control system

Abstract

Devices, systems, and methods are provided for testing and validation of a camera. A device may capture a first image of a target using a camera, wherein the camera is in a clean state, and wherein the target is in a line of sight of the camera. The device may apply an obstruction to a portion of a lens of the camera. The device may apply a camera cleaning system to the lens of the camera. The device may capture a post-clean image after applying the camera cleaning system. The device may determine a post-clean SSIM score based on comparing the post clean image to the first image. The device may compare the post-clean SSIM score to a validation threshold. The device may determine a validation state of the camera cleaning system based on the comparison.

IPC Classes  ?

• H04N 17/00 - Diagnosis, testing or measuring for television systems or their details
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06F 18/22 - Matching criteria, e.g. proximity measures

Abstract

Systems, methods, and computer-readable media are disclosed for a systems and methods for improved LIDAR return light capture efficiency. One example method may include comparing, by a controller including a processor and at a first time, a first temperature of a first computing element to a first threshold temperature and a second temperature of a second computing element to a second threshold temperature. The example method may also include sending, based on a determination that the first temperature is below the first threshold temperature and the second temperature is above the second threshold temperature, a first signal to a switch to activate a data output corresponding to the second computing element. The example method may also include sending, to the second computing element, a second signal to cause a third computing element to increase heat dissipation from the third computing element to the first computing element. The example method may also include receiving, from the first computing element, a third temperature of the first computing element at a second time. The example method may also include comparing the third temperature of the first computing element to the first threshold temperature. The example method may also include determining that the third temperature of the first computing element is at or above the first threshold temperature at the second time. The example method may also include sending, based on a determination that that the third temperature is at or above the first threshold temperature, a third signal to the switch to activate a data output corresponding to the first computing element.

IPC Classes  ?

• G06F 1/00 - Details not covered by groups and
• G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
• G06F 1/20 - Cooling means
• G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
• H05K 7/20 - Modifications to facilitate cooling, ventilating, or heating

Abstract

A system will generate a vector map of a geographic area using a method that includes receiving a birds-eye view image of a geographic area. The birds-eye view image comprises various pixels. The system will process the birds-eye view image to generate a spatial graph representation of the geographic area, and it will save the node pixels and the lines to a vector map data set. The processor may be a component of a vehicle such as an autonomous vehicle. If so, the system may use the vector map data set to generate a trajectory for the vehicle as the vehicle moves in the geographic area.

IPC Classes  ?

• G01C 21/00 - NavigationNavigational instruments not provided for in groups
• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G01C 21/34 - Route searchingRoute guidance
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06F 18/24 - Classification techniques
• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods
• G06V 10/40 - Extraction of image or video features
• G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Abstract

Systems and methods for managing data. The methods comprise: receiving, by a load driver node of a network, a request for upload of source data associated with a resource name to a data warehouse; generating a publication identifier and a version value for the source data by the load driver node in response to the request; causing, by the load driver node, operations to be performed by load worker nodes to facilitate population of at least one fact table of the data warehouse with the publication identifier and the source data; and causing, by the load driver node, a publication table of the data warehouse to be updated to include the publication identifier and the version value so as to be associated with the resource name.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
• G06F 16/21 - Design, administration or maintenance of databases

Abstract

Systems for managing access to an autonomous vehicle includes an autonomous vehicle including a plurality of storage compartments, wherein each of the plurality of storage compartments comprises a locking mechanism and at least one processor to receive data associated with an item to be positioned in a storage compartment of the plurality of storage compartments, determine that one of the plurality of storage compartments has storage capacity for the item, designate one of the plurality of storage compartments for storage of the item, activate the locking mechanism of the designated storage compartment to lock the designated storage compartment after the item is positioned in the designated storage compartment, and activate the locking mechanism of the designated storage compartment to unlock the designated storage compartment to allow removal of the item from the designated storage compartment. Methods, computer program products, and autonomous vehicles are also disclosed.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• G05D 1/02 - Control of position or course in two dimensions
• B60R 25/24 - Means to switch the anti-theft system on or off using electronic identifiers containing a code not memorised by the user

Abstract

Systems and methods for managing data. The methods comprise by a computing system: generating publication identifiers and version values for source data to be stored into a data warehouse; causing a plurality of fact tables in the data warehouse to be populated with the source data and the publication identifiers; causing a publication table in the data warehouse to be updated to include the publication identifiers and the version values so as to be respectively associated with resource names; receiving a query for information directed to the plurality of fact tables; retrieving the publication identifiers from the publication table, in response to the query; and obtaining source data from each said fact table of the plurality of fact tables that is associated with publication identifiers that are stored in both the fact table and the publication table.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/245 - Query processing
• G06F 16/248 - Presentation of query results
• G05D 1/02 - Control of position or course in two dimensions

Abstract

Systems and methods for retrieving and using data stored in a data warehouse. The methods comprise performing the following operations by a computing device: receiving a query for information directed to a plurality of fact tables (each fact table comprising sets of source data that are arranged so as to be respectively associated with a plurality of first publication identifiers); obtaining second publication identifiers from a publication table, in response to the query; and obtaining at least one of the sets of source data from each fact table that is associated with a first publication identifier of the plurality of first publication identifiers which matches one of the second publication identifiers.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 16/2455 - Query execution
• G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
• G06F 16/21 - Design, administration or maintenance of databases

Abstract

A ride service system will determine a stopping location for an autonomous vehicle (AV) before the AV picks up a passenger in response to a ride service request. The system will determine a pickup area for the request, along with a loading point within a pickup area, and the AV will navigate along the route toward the pickup area. Before the AV reaches the pickup area, the system will determine whether it received a departure confirmation indicating that the passenger is at the loading point. If the system received the departure confirmation, the AV will navigate into the pickup area and stop at the loading point; otherwise, the AV will either (a) navigate to an intermediate stopping location before reaching the pickup area or (b) pass through the pickup area.

IPC Classes  ?

• G01C 21/34 - Route searchingRoute guidance
• G01C 21/36 - Input/output arrangements for on-board computers
• G06Q 10/02 - Reservations, e.g. for tickets, services or events
• G06Q 50/40 - Business processes related to the transportation industry

Abstract

This document describes methods and systems for enabling an autonomous vehicle (AV) to determine a path to a stopping location. The AV will determine a desired stop location (DSL) that is associated with a service request. The AV's motion control system will move the AV along a path to the DSL. While moving along the path, the AV's perception system will detect ambient conditions near the DSL. The ambient conditions will be parameters associated with a stopping rule. The AV will apply the stopping rule to the ambient conditions to determine whether the stopping rule permits the AV to stop at the DSL. If the stopping rule permits the AV to stop at the DSL, the motion control system will move the AV to, and stop at, the DSL. Otherwise, the motion control system will not stop the AV at the DSL.

IPC Classes  ?

• B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
• G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
• B60W 30/18 - Propelling the vehicle
• B60W 40/04 - Traffic conditions