Example embodiments relate to frequency selective structures for automotive radar. A system may include a frequency selective structure that is coupled to a radome and configured to optimize a desired band-pass for specific performance by one or more antennas. The frequency selective structure may comprise arrays of micro-wires forming a plurality of intersections and one or more patches positioned at one or more intersections of the plurality of intersections. In some cases, at least one patch of the one or more patches may be positioned with an offset relative to a corresponding intersection of the one or more intersections and the frequency selective structure has a rotational angle with respect to an antenna polarization of the one or more antennas. The frequency selective structure may be connected to a current source that can modify the temperature of the structure to melt rain or snow off the radome.
Example embodiments relate to a sensor unit with rotating housing and spoiler for enhanced airflow. An example device includes one or more sensors configured to sense one or more aspects of an environment surrounding the device. The device also includes a housing that at least partially surrounds the one or more sensors. The housing and the one or more sensors are configured to rotate about a shared axis. The housing includes an inlet configured to act as an air intake for an airflow through the housing. The airflow is configured to cool the one or more sensors while the one or more sensors are operating. Further, the device includes a spoiler positioned on or near the inlet. The spoiler is configured to increase an air pressure near the inlet or promote laminar flow near the inlet in order to promote the airflow through the housing.
Aspects of the disclosure provide a method of facilitating communications from an autonomous vehicle to a user. For instance, a method may include, while attempting to pick up the user and prior to the user entering an vehicle, inputting a current location of the vehicle and map information into a model in order to identify a type of communication action for communicating a location of the vehicle to the user; enabling a first communication based on the type of the communication action; determining whether the user has responded to the first communication from received sensor data; and enabling a second communication based on the determination of whether the user has responded to the communication.
B60Q 5/00 - Arrangement or adaptation of acoustic signal devices
B60Q 1/26 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic
G05D 1/81 - Handing over between on-board automatic and on-board manual control
G08G 1/00 - Traffic control systems for road vehicles
4.
Display screen or portion thereof with icon for a vehicle
Aspects of the technology relate to exception handling for a vehicle. For instance, a current trajectory for the vehicle and sensor data corresponding to one or more objects may be received. Based on the received sensor data, projected trajectories of the one or more objects may be determined. Potential collisions with the one or more objects may be determined based on the projected trajectories and the current trajectory. One of the potential collisions that is earliest in time may be identified. Based on the one of the potential collisions, a safety-time-horizon (STH) may be identified. When a runtime exception occurs, before performing a precautionary maneuver to avoid a collision, waiting no longer than the STH for the runtime exception to resolve.
Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, one or more processors of one or more first systems of the autonomous vehicles may receive a signal indicating a predicted traffic stack for a lane in which the autonomous vehicle is currently traveling. In response to the received signal, costs of edges of a roadgraph between the autonomous vehicle and a location of the predicted traffic stack may be adjusted in order to encourage the autonomous vehicle to change lanes in response to the predicted traffic stack. A route may be generated to a destination based on at least one of the adjusted costs. The route may be provided to one or more second systems of the autonomous vehicle in order to control the autonomous vehicle according to the route.
The present disclosure relates to systems and methods that provide an accurate angle measurement of a rotatable mirror. An example method includes causing a light-emitter device to emit emission light along an optical axis toward a rotatable mirror, such that the emission light interacts with a reflective surface of the rotatable mirror to provide reflected light. The rotatable mirror is configured to rotate about a rotational axis. The method also includes receiving, from a detector device, a reflected light signal. The method also includes determining, by a detector readout circuit and based on the reflected light signal, a rotational angle of the rotatable mirror. Determining the rotational angle of the rotatable mirror involves providing, by a digital comparator, a digital signal comprising information indicative of rising and falling edges of an analog signal based on a current pulse from the detector device.
Example embodiments relate to methods and systems for using interference to detect sensor impairment. Radar or another type of sensor on a vehicle may receive radio-frequency (RF) signals propagating in the environment. These RF signals may originate from an external source and a computing device can be used to determine a distance and an angle to the source in order to identify a power level threshold that represents an expected power associated with the RF signals. The computing device may then perform a comparison between a power level of the RF signals and a power level threshold. Based on the comparison, the computing device may decrease a confidence assigned to the radar coupled to the vehicle and control the vehicle based on the decreased confidence assigned to the radar.
A system may include a common processed data pipeline and a plurality of processors. Outputs of the plurality of processors are communicatively coupled to the common processed data pipeline. Each processor is configured to accept a plurality of input signals, and each of the plurality of input signals represents a light signal detected by a photodetector. Each processor is also configured to process the input signals to provide processed data. Each processor is also configured to output the processed data into one or more predetermined data locations of a data stream of the common processed data pipeline.
Aspects of the disclosure relate to determining perceptive range of a vehicle in real time. For instance, a static object defined in pre-stored map information may be identified. Sensor data generated by a sensor of the vehicle may be received. The sensor data may be processed to determine when the static object is first detected in an environment of the vehicle. A distance between the object and a location of the vehicle when the static object was first detected may be determined. This distance may correspond to a perceptive range of the vehicle with respect to the sensor. The vehicle may be controlled in an autonomous driving mode based on the distance.
B60R 11/04 - Mounting of cameras operative during driveArrangement of controls thereof relative to the vehicle
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
16.
INCONVENIENCE FOR PASSENGER PICKUPS AND DROP OFFS FOR AUTONOMOUS VEHICLES
Aspects of the disclosure relate to generating map data. For instance, data generated by a perception system of a vehicle may be received. This data corresponds to a plurality of observations including observed positions of a passenger of the vehicle as the passenger approached the vehicle at a first location. The data may be used to determine an observed distance traveled by a passenger to reach a vehicle. A road edge distance between an observed position of an observation of the plurality of observations and a nearest road edge to the observed position may be determined. An inconvenience value for the first location may be determined using the observed distance and the road edge distance. The map data is then generated using the inconvenience value.
Aspects of the disclosure relate to generating scouting objectives in order to update map information used to control a fleet of vehicles in an autonomous driving mode. For instance, a notification from a vehicle of the fleet identifying a feature and a location of the feature may be received. A first bound for a scouting area may be identified based on the location of the feature. A second bound for the scouting area may be identified based on a lane closest to the feature. A scouting objective may be generated for the feature based on the first bound and the second bound.
An autonomous vehicle may include a stuck condition detection component and a communications component. The stuck-detection component may be configured to detect a condition in which the autonomous vehicle is impeded from navigating according to a first trajectory. The communications component may send an assistance signal to an assistance center and receive a response to the assistance signal. The assistance signal may include sensor information from the autonomous vehicle. The assistance center may include a communications component and a trajectory specification component. The communications component may receive the assistance signal and send a corresponding response. The trajectory specification component may specify a second trajectory for the autonomous vehicle and generate the corresponding response that includes a representation of the second trajectory. The second trajectory may be based on the first trajectory and may ignore an object that obstructs the first trajectory.
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
B60W 30/00 - Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for anomaly estimation for behavior predictions using a neural network. One of the methods includes receiving data characterizing a scene that includes an agent in an environment. A behavior prediction input generated from the data is processed using a behavior prediction model. The behavior prediction model is configured to process the behavior prediction input to generate a predicted probability distribution over a plurality of possible behaviors for the agent. An anomaly estimation input generated from the data is processed using an anomaly estimation model. The anomaly estimation model is configured to process the anomaly estimation input to generate a prediction error for the predicted probability distribution. The prediction error indicates an error between the predicted probability distribution generated by the behavior prediction model and another predicted probability distribution generated by another behavior prediction model.
The described aspects and implementations use artificial intelligence (AI) to detect passengers in a vehicle. A method of an implementation includes obtaining one or more images captured by one or more cameras of a vehicle. The method includes generating, using one or more artificial intelligence models and the one or more images, passenger data indicating locations of one or more passengers of the vehicle. The method includes generating, based on the passenger data, vehicle area data indicating one or more areas of the vehicle at which the one or more passengers are located. The method includes determining, based on the passenger data and the vehicle area data, whether at least one passenger seating configuration criterion is satisfied. The method includes, responsive to determining that such a criterion is satisfied, causing the vehicle to perform an action associated with a passenger seating configuration in the vehicle.
The technology uses augmented reality (AR) elements for enhanced wayfinding with autonomous vehicle pickups and drop-offs. The approach includes generating, for presentation in a first region of a client device UI, trip information regarding a trip. Map information associated with the trip is generated for presentation in a second UI region, including at least one of a pickup location for a rider, a walking path from a current location of the rider to the pickup location, a planned route of the vehicle to the pickup location, or a current location of the vehicle. An AR indicator is generated for presentation in the second UI region. Upon selection of the indicator, the system modifies the second region into a first section to display at least a portion of the map information and a second section to display an augmented reality view, or replace the map information with the AR view.
The technology relates to an exterior sensor system for a vehicle configured to operate in an autonomous driving mode. The technology includes a close-in sensing (CIS) camera system to address blind spots around the vehicle. The CIS system is used to detect objects within a few meters of the vehicle. Based on object classification, the system is able to make real-time driving decisions. Classification is enhanced by employing cameras in conjunction with lidar sensors. The specific arrangement of multiple sensors in a single sensor housing is also important to object detection and classification. Thus, the positioning of the sensors and support components are selected to avoid occlusion and to otherwise prevent interference between the various sensor housing elements.
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
H04N 23/74 - Circuitry for compensating brightness variation in the scene by influencing the scene brightness using illuminating means
23.
Methods and Systems for Automatic Introspective Perception
Example embodiments relate to self-supervisory and automatic response techniques and systems. A computing system may use sensor data from an autonomous vehicle sensor to detect an object in the environment of the vehicle as the vehicle navigates a path. The computing system may then determine a detection distance between the object and the sensor responsive to detecting the object. The computing system may then perform a comparison between the detection distance and a baseline detection distance that depends on one or more prior detections of given objects that are in the same classification group as the object. The computing system may then adjust a control strategy for the vehicle based on the comparison.
Aspects of the disclosure relate cleaning systems for cleaning cabin air and interior surfaces of a vehicle. For instance, a cleaning system may include a surface cleaning device including a UVC light source. In addition, a request for confirmation that the vehicle may not be occupied may be sent to a remote computing device. In response to the request, a signal indicating whether or not the vehicle is occupied may be received. The surface cleaning device may then be activated based on the signal.
An optical receiver includes a plurality of photodetectors, a shared memory, and a pulse calibration processing unit communicatively coupled to the shared memory. The optical receiver also includes a hardware accelerator module configured to accept input waveforms from the plurality of photodetectors and compare an amplitude of the respective input waveforms with a predetermined threshold. Based on the comparison, the hardware accelerator module could determine subsets of the input waveforms and determine information indicative of characteristic aspects of the subsets of the input waveforms. The optical receiver is additionally operable to store the determined information in the shared memory and trigger an interrupt for the pulse calibration processing unit to initiate a pulse calibration process on the determined information.
The technology relates to an exterior sensor system for a vehicle configured to operate in an autonomous driving mode. The technology includes a close-in sensing (CIS) camera system to address blind spots around the vehicle. The CIS system is used to detect objects within a few meters of the vehicle. Based on object classification, the system is able to make real-time driving decisions. Classification is enhanced by employing cameras in conjunction with lidar sensors. The specific arrangement of multiple sensors in a single sensor housing is also important to object detection and classification. Thus, the positioning of the sensors and support components are selected to avoid occlusion and to otherwise prevent interference between the various sensor housing elements.
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
B60R 11/04 - Mounting of cameras operative during driveArrangement of controls thereof relative to the vehicle
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
H04N 23/80 - Camera processing pipelinesComponents thereof
A system includes a control circuit and a LLC resonant power converter. The control circuit is configured to generate a first gate drive signal to initiate operation of the LLC resonant power converter at a first frequency. The LLC resonant power converter is configured to drive a wireless power signal at a primary winding of a rotary power transformer disposed on a first platform and transmit the wireless power signal across a gap separating the first platform and a second platform that rotates relative to the first platform. The LLC resonant power converter is configured to operate, in an open loop mode without feedback control, a device mounted on the second platform. In response to satisfaction of a condition, the control circuit is configured to generate a second gate drive signal to operate the LLC resonant power converter at a second frequency that is lower than the first frequency.
H02M 1/32 - Means for protecting converters other than by automatic disconnection
H02M 1/08 - Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
H02M 3/335 - Conversion of DC power input into DC power output with intermediate conversion into AC by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate AC using devices of a triode or a transistor type requiring continuous application of a control signal using semiconductor devices only
Aspects of the disclosure provide for generation of trajectories for a vehicle driving in an autonomous driving mode. For instance, information identifying a plurality of objects in the vehicle's environment and a confidence value for each of the objects is received. A set of constraints may be generated. That one or more processors are unable to solve for a trajectory given the set of constraints and an acceptable braking limit may be determined. A first constraint is identified as a constraint for which could not be solved and a first confidence value. That the vehicle should apply a maximum braking level is determined based on the identified first confidence value, a threshold, and the determination that the one or more processors are unable to solve for a trajectory. Based on the determination that the vehicle should apply the maximum braking level, the maximum braking level is applied.
B60T 8/171 - Detecting parameters used in the regulationMeasuring values used in the regulation
B60T 7/22 - Brake-action initiating means for automatic initiationBrake-action initiating means for initiation not subject to will of driver or passenger initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle
B60T 8/17 - Using electrical or electronic regulation means to control braking
B60T 8/58 - Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force responsive to a speed condition, e.g. acceleration or deceleration responsive to speed and another condition or to plural speed conditions
29.
Managing and tracking scouting tasks using autonomous vehicles
A method is provided for managing and tracking scouting tasks to obtain map information using a fleet of autonomous vehicles. For instance, the method includes defining a scouting quest to obtain the map information. The scouting quest includes a plurality of objectives. Each objective is associated with a geographic location from which sensor data is to be captured. The method also includes receiving a first update message from an autonomous vehicle of the fleet. The update message identifies a location of the autonomous vehicle. The method also includes assigning at least one of the objectives to the autonomous vehicle based on the location of the autonomous vehicle. The method also includes sending instructions to the autonomous vehicle in order to cause the autonomous vehicle to complete the at least one objective and after sending, tracking a status of the scouting quest.
A light detection and ranging (LIDAR) device scans through a scanning zone while emitting light pulses and receives reflected signals corresponding to the light pulses. The LIDAR device scans the emitted light pulses through the scanning zone by reflecting the light pulses from an array of oscillating mirrors. The mirrors are operated by a set of electromagnets arranged to apply torque on the mirrors, and an orientation feedback system senses the orientations of the mirrors. Driving parameters for each mirror are determined based on information from the orientation feedback system. The driving parameters can be used to drive the mirrors in phase at an operating frequency despite variations in moments of inertia and resonant frequencies among the mirrors.
The disclosed systems and techniques facilitate efficient detection and navigation of reduced drivability areas in driving environments. The disclosed techniques include, obtaining, using a sensing system of a vehicle, a set of camera images, a set of radar images, and/or a set of lidar images of an environment. The techniques further include generating, using a first neural network (NN), camera feature(s) characterizing the camera images, generating, using a second NN, radar features characterizing the radar images, and/or generating, using a third NN, lidar feature(s) characterizing the lidar images. The techniques further include processing the camera feature(s), the radar feature(s), and the lidar feature(s) to obtain an indication of a reduced drivability area in the environment.
G01C 21/28 - NavigationNavigational instruments not provided for in groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06T 7/174 - SegmentationEdge detection involving the use of two or more images
G06T 7/62 - Analysis of geometric attributes of area, perimeter, diameter or volume
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
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 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
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
32.
Single Antenna with Dual Circular Polarizations and Quad Feeds for Millimeter Wave Applications
Example embodiments relate to a substrate integrated waveguide (SIW) with dual circular polarizations. An example SIW may include a dielectric substrate and a first metallic layer coupled to a top surface of the dielectric substrate with a through-hole extending through the dielectric substrate and the first metallic layer. The SIW also includes a dielectric layer coupled to a top surface of the first metallic layer. A second metallic layer is coupled to a top surface of the dielectric layer. The second metallic layer includes a non-conductive opening, a plurality of feeds with a first end in the non-conductive opening and a second end including a single-ended termination, and an impedance transformer. The SIW also includes a third metallic layer coupled to a bottom of the dielectric substrate, and a set of metallic via-holes proximate the non-conductive opening and coupling the second metallic layer to the third metallic layer.
H01P 3/16 - Dielectric waveguides, i.e. without a longitudinal conductor
H01Q 1/50 - Structural association of antennas with earthing switches, lead-in devices or lightning protectors
H01Q 21/24 - Combinations of antenna units polarised in different directions for transmitting or receiving circularly and elliptically polarised waves or waves linearly polarised in any direction
33.
MULTIMODE LIDAR RECEIVER FOR COHERENT DISTANCE AND VELOCITY MEASUREMENTS
The subject matter of this specification can be implemented in, among other things, systems and methods that enable lidar devices capable of detecting and processing multiple optical modes present in a beam reflected from a target object. Different received optical modes can be spatially separated and electronic signals can be generated that are representative of a coherence information contained in various optical modes. Multiple generated electronic signals can be amplified, phase-shifted, mixed, etc., to identify signals, individually or in a combination, that can be used for identification of a range and velocity of the target object with the highest accuracy.
A computer-implemented method is provided that involves determining, based on map data, an approaching merge region comprising an on-ramp merging with a road comprising one or more lanes, wherein a truck is traveling on an initial lane of the road according to a navigation plan. The method involves an indication of movement of a vehicle on the on-ramp, wherein the indication of movement is based on data collected by one or more sensors configured to capture sensor data from an environment surrounding the truck. The method involves determining, for the on-ramp and the one or more lanes, respective avoidance scores indicative of a likelihood of an interaction between the truck and the vehicle based on the approaching merge region. The method involves updating the navigation plan based on the respective avoidance scores. The method also involves controlling the truck to execute a driving strategy based on the updated navigation plan.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
35.
DETERMINING CHANGES IN A DRIVING ENVIRONMENT BASED ON VEHICLE BEHAVIOR
A method and apparatus are provided for determining whether a driving environment has changed relative to previously stored information about the driving environment. The apparatus may include an autonomous driving computer system configured to detect one or more vehicles in the driving environment, and determine corresponding trajectories for those detected vehicles. The autonomous driving computer system may then compare the determined trajectories to an expected trajectory of a hypothetical vehicle in the driving environment. Based on the comparison, the autonomous driving computer system may determine whether the driving environment has changed and/or a probability that the driving environment has changed, relative to the previously stored information about the driving environment.
An example method includes receiving, from a light detector, information indicative of a light intensity of a field of view of an optical system. The optical system includes one or more optical components and a light detector configured to receive light from a field of view of an environment of the optical system by way of the one or more optical components. The optical system also includes an occlusion-detection camera configured to capture images of the one or more optical components. The example method also includes adjusting, based on the received information, at least one operating parameter of the occlusion-detection camera. The example method also includes causing the occlusion-detection camera to capture at least one image of the one or more optical components according to the at least one adjusted operating parameter.
G01S 7/4863 - Detector arrays, e.g. charge-transfer gates
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
H04N 23/45 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from two or more image sensors being of different type or operating in different modes, e.g. with a CMOS sensor for moving images in combination with a charge-coupled device [CCD] for still images
H04N 23/73 - Circuitry for compensating brightness variation in the scene by influencing the exposure time
37.
TRAINING AGENT TRAJECTORY PREDICTION NEURAL NETWORKS USING DISTILLATION
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training trajectory prediction neural networks using distillation.
In one example, a LIDAR device includes a light sources that emits light and a transmit lens that directs the emitted light to illuminate a region of an environment with a field-of-view defined by the transmit lens. The LIDAR device also includes a receive lens that focuses at least a portion of incoming light propagating from the illuminated region of the environment along a predefined optical path. The LIDAR device also includes an array of light detectors positioned along the predefined optical path. The LIDAR device also includes an offset light detector positioned outside the predefined optical path. The LIDAR device also includes a controller that determines whether collected sensor data from the array of light detectors includes data associated with another light source different than the light source of the device based on output from the offset light detector.
G01S 7/48 - Details of systems according to groups , , of systems according to group
G01S 17/04 - Systems determining the presence of a target
G01S 17/42 - Simultaneous measurement of distance and other coordinates
G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
G05D 1/247 - Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons
The technology relates to controlling a vehicle in an autonomous driving mode in accordance with behavior predictions for other road users in the vehicle's vicinity. In particular, the vehicle's onboard computing system may predict whether another road user will perform a “continuing” lane driving operation, such as going straight in a turn-only lane. Sensor data from detected/observed objects in the vehicle's nearby environment may be evaluated in view of one or more possible behaviors for different types of objects. In addition, roadway features, in particular whether lane segments are connected in a roadgraph, are also evaluated to determine probabilities of whether other road users may make an improper continuing lane driving operation. This is used to generate more accurate behavior predictions, which the vehicle can use to take alternative (e.g., corrective) driving actions.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
40.
SELECTIVE DEACTIVATION OF LIGHT EMITTERS FOR INTERFERENCE MITIGATION IN LIGHT DETECTION AND RANGING (LIDAR) DEVICES
Example embodiments relate to selective deactivation of light emitters for interference mitigation in light detection and ranging (lidar) devices. An example method includes deactivating one or more light emitters within a lidar device during a firing cycle. The method also includes identifying whether interference is influencing measurements made by the lidar device. Identifying whether interference is influencing measurements made by the lidar device includes determining, for each light detector of the lidar device that is associated with the one or more light emitters deactivated during the firing cycle, whether a light signal was detected during the firing cycle.
Aspects of the disclosure relate controlling autonomous vehicles or vehicles having an autonomous driving mode. More particularly, these vehicles may identify and respond to other vehicles engaged in a parallel parking maneuver by receiving sensor data corresponding to objects in an autonomous vehicle's environment and including location information for the objects over time. An object corresponding to another vehicle in a lane in front of the first vehicle may be identified from the sensor data. A pattern of actions of the other vehicle identified form the sensor data is used to determine that the second vehicle is engaged in a parallel parking maneuver based on a pattern of actions exhibited by the other vehicle identified from the sensor data. The determination is then used to control the autonomous 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
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
42.
ARRANGING PASSENGER PICKUPS FOR AUTONOMOUS VEHICLES
Aspects of the disclosure relate to arranging a pick up and drop off locations between a driverless vehicle and a passenger. As an example, a method of doing so may include receiving a request for a vehicle from a client computing device, wherein the request identifies a first location. Pre-stored map information and the first location are used to identify a recommended point according to a set of heuristics. Each heuristic of the set of heuristics has a ranking such that the recommended point corresponds to a location that satisfies at least one of the heuristics having a first rank and such that no other location satisfies any other heuristic of the set of heuristics having a higher rank than the first rank. The pre-stored map information identifying a plurality of pre-determined locations for the vehicle to stop, and the recommended point is one of the plurality of pre-determined locations. The recommended point is then provided the client computing device for display on a display of the client computing device with a map.
Example embodiments relate to techniques for adjusting vehicle behavior based on estimated unintentional lateral movements. A computing system may receive sensor data representing a truck's environment as the truck pulls a trailer and navigates above a threshold speed on a freeway or another type of road. The computing system may use the sensor data to detect another truck navigating in an adjacent lane and at a speed that indicates an increased likelihood of a pass maneuver occurring between the trucks. Responsive to determining the increased likelihood of the pass maneuver occurring between the vehicles, the computing system may estimate an unintentional lateral movement for the pass maneuver based on parameters that can include the truck's speed, the size of the other truck's trailer, and a wind condition of the environment. The computing system can subsequently control the truck based on estimated unintentional lateral movement for the pass maneuver.
Example embodiments relate to light detection and ranging (lidar) devices having a light-guide manifold. An example lidar device includes a transmit subsystem. The transmit subsystem includes a light emitter. The transmit subsystem also includes a light-guide manifold optically coupled to the light emitter. Further, the transmit subsystem includes a telecentric lens assembly optically coupled to the light-guide manifold. The lidar device also includes a receive subsystem. The receive subsystem includes the telecentric lens assembly. The receive subsystem also includes an aperture plate having an aperture defined therein. The aperture plate is positioned at a focal plane of the telecentric lens assembly. Further, the receive subsystem includes a silicon photomultiplier (SiPM) positioned to receive light traveling through the aperture.
G01F 23/00 - Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
G01S 7/481 - Constructional features, e.g. arrangements of optical elements
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
The technology employs a holistic approach to passenger pickups and other wayfinding situations. This includes identifying where passengers are relative to the vehicle and/or the pickup location. One aspect leverages camera imagery from a rider's client device when providing rider support. This enables an agent to receive the imagery to help guide the rider to the vehicle. Another aspect provides audio information to the rider to help them get to the vehicle. This can include selecting or curating various tones or melodies, giving the rider advanced notice of sounds to listen for, and modifying sounds as the rider approaches the vehicle or to address ambient noise in the environment. Different wayfinding tools may be selected for presentation to the rider based on their proximity state to a pickup location or to the vehicle. Gracefully transitioning between different tools can enhance their usefulness and elevate the rider's experience for a trip.
The technology relates to autonomous vehicles for transporting cargo and/or people between locations. Distributed sensor arrangements may not be suitable for vehicles such as large trucks, busses or construction vehicles. Side view mirror assemblies are provided that include a sensor suite of different types of sensors, including LIDAR, radar, cameras, etc. Each side assembly is rigidly secured to the vehicle by a mounting element. The sensors within the assembly may be aligned or arranged relative to a common axis or physical point of the housing. This enables self-referenced calibration of all sensors in the housing. Vehicle-level calibration can also be performed between the sensors on the left and right sides of the vehicle. Each side view mirror assembly may include a conduit that provides one or more of power, data and cooling to the sensors in the housing.
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
B60R 1/12 - Mirror assemblies combined with other articles, e.g. clocks
G01S 7/481 - Constructional features, e.g. arrangements of optical elements
G01S 13/86 - Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
G01S 13/931 - Radar or analogous systems, specially adapted for specific applications for anti-collision purposes of land vehicles
G01S 17/86 - Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
G01S 17/87 - Combinations of systems using electromagnetic waves other than radio waves
G01S 17/931 - Lidar systems, specially adapted for specific applications for anti-collision purposes of land vehicles
G05D 1/247 - Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons
G05D 1/249 - Arrangements for determining position or orientation using signals provided by artificial sources external to the vehicle, e.g. navigation beacons from positioning sensors located off-board the vehicle, e.g. from cameras
G05D 1/43 - Control of position or course in two dimensions
G05D 101/20 - Details of software or hardware architectures used for the control of position using external object recognition
Aspects of the disclosure provide for reducing inconvenience to other road users caused by stopped autonomous vehicles. As an example, a vehicle having an autonomous driving mode may be stopped at a first location. While the vehicle is stopped, sensor data is received from a perception system of the vehicle. The sensor data may identify a road user. Using the sensor data, a value indicative of a level of inconvenience to the road user caused by stopping the vehicle at the first location may be determined. The vehicle is controlled in the autonomous driving mode to cause the vehicle to move from the first location and in order to reduce the value.
Example embodiments relate to wiper devices for cleaning sensor housings. An example embodiment includes a wiper device for cleaning a surface of a rotatable sensor housing. The wiper device may have a driving arm having a first end and a second end. The first end of the driving arm is connected to a rotatable shaft. The wiper device may also have a wiper arm pivotally connected to the second end of the driving arm. The pivotal connection is located between a front region and a rear region of the wiper arm. Further, the wiper device may have a wiper blade coupled to the front region of the wiper arm. The wiper blade may be configured to wipe the surface of the rotatable sensor housing. Additionally, the wiper device may have a counterweight coupled to the rear region of the wiper arm.
B60S 1/60 - Cleaning windscreens, windows, or optical devices specially adapted for cleaning other parts or devices than front windows or windscreens for signalling devices, e.g. reflectors
The technology relates to detection of aberrant driving situations during operation of a vehicle in an autonomous driving mode. Aberrant situations may include potential theft or unsafe conditions, which are determined according to one or more signals. The signals are derived from information detected about the environment around the vehicle, such as from one or more sensors disposed on the vehicle. In response to an aberrant situation, the vehicle may take various corrective action, such as rerouting, locking down the vehicle or communicating with remote assistance. The type of corrective action taken may depend on a type of cargo being transported or whether one or more passengers are in the vehicle. If there are passengers, the system may communicate with the passengers via the passenger's client computing devices or by presenting visual or audible information via a user interface system of the vehicle.
B60R 25/102 - Fittings or systems for preventing or indicating unauthorised use or theft of vehicles actuating a signalling device a signal being sent to a remote location, e.g. a radio signal being transmitted to a police station, a security company or the owner
B60R 25/30 - Detection related to theft or to other events relevant to anti-theft systems
B60R 25/31 - Detection related to theft or to other events relevant to anti-theft systems of human presence inside or outside the vehicle
B60W 30/09 - Taking automatic action to avoid collision, e.g. braking and steering
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
G08G 1/127 - Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles to a central station
Aspects of the disclosure provide for arranging tour trips. For instance, a request identifying a pickup location for a tour trip may be received from a client computing device. A set of tours may be selected from a plurality of pre-stored tours based on the pickup location. Each of the plurality of pre-stored tours may include tour locations. The set of tours may be provided to the client computing device for display. Information identifying one of the set of tours may be received from the client computing device. Instructions may be provided to an autonomous vehicle to cause the autonomous vehicle to complete the tour trip by maneuvering to the pickup location and thereafter visit one or more tour locations included in the identified one of the set of tours.
A sensor module comprising a housing defining an internal cavity having a proximal opening and a distal opening, a first barrier that covers the proximal opening of the internal cavity, a second barrier that is positioned between the first barrier and the proximal opening of the internal cavity, at least one microphone acoustically coupled to the distal opening of the internal cavity, and a porous plastic material that fills a particular portion of the internal cavity located between the proximal opening and the distal opening.
Aspects of the disclosure provide for controlling an autonomous vehicle to respond to queuing behaviors at pickup or drop-off locations. As an example, a request to pick up or drop off a passenger at a location may be received. The location may be determined to likely have a queue for picking up and dropping off passengers. Based on sensor data received from a perception system, whether a queue exists at the location may be determined. Once it is determined that a queue exists, it may be determined whether to join the queue to avoid inconveniencing other road users. Based on the determination to join the queue, the vehicle may be controlled to join the queue.
Methods and devices for using a relationship between activities of different traffic signals in a network to improve traffic signal state estimation are disclosed. An example method includes determining that a vehicle is approaching an upcoming traffic signal. The method may further include determining a state of one or more traffic signals other than the upcoming traffic signal. Additionally, the method may also include determining an estimate of a state of the upcoming traffic signal based on a relationship between the state of the one or more traffic signals other than the upcoming traffic signal and the state of the upcoming traffic signal.
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 40/00 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit
G08G 1/00 - Traffic control systems for road vehicles
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
Aspects of the disclosure provide for arranging a trip in an autonomous vehicle without a driver. For instance, a request may be received from a client computing device associated with a first person to arrange the trip for a second person. The request may include a pickup location for the second person and a destination location for the second person. An authentication method may be identified for the trip. A signal may be sent to the autonomous vehicle in order to cause the autonomous vehicle to maneuver to the pickup location, authenticate the second person using the authentication method, and transport the second person to the destination location.
B60R 25/24 - Means to switch the anti-theft system on or off using electronic identifiers containing a code not memorised by the user
B60R 25/30 - Detection related to theft or to other events relevant to anti-theft systems
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
56.
DYNAMIC SENSING CHANNEL MULTIPLEXING FOR LIDAR APPLICATIONS
The subject matter of this specification can be implemented in, among other things, a system that includes a light source to produce a first beam, a diffraction optical element (DOE) to generate, based on the first beam configured to have a first phase information, one or more second beams. The system further includes a DOE control module to configure the DOE, for each of a plurality of times, into a respective one of a plurality of DOE configurations, and cause each of the one or more second beams to have a phase information that is different from a phase information of the first beam, wherein the phase information of each of the one or more second beams is determined by a time sequence of the plurality of DOE configurations.
The described aspects and implementations enable fast and accurate object identification in autonomous vehicle (AV) applications by combining radar data with camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar image of a first hypothetical object in an environment of the AV, obtaining a camera image of a second hypothetical object in the environment of the AV, and processing the radar image and the camera image using one or more machine learning models MLMs to obtain a prediction measure representing a likelihood that the first hypothetical object and the second hypothetical object correspond to a same object in the environment of the AV.
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G01S 7/41 - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisationTarget signatureTarget cross-section
G01S 13/86 - Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
G01S 13/931 - Radar or analogous systems, specially adapted for specific applications for anti-collision purposes of land vehicles
A system includes a first autonomous vehicle (AV) including a sensing system including a set of sensors, a memory storing instructions and a processing device operatively coupled to the memory, wherein the instructions, when executed by the processing device, cause the processing device to perform operations including observing, using data obtained from the sensing system, an event reflecting a scenario within a driving environment, generating a set of event observation data characterizing the event, and causing the set of event observation data to be shared with at least a second AV that is determined to be in a vicinity of the event after the event is observed.
A method of updating a clock associated with an autonomous vehicle includes determining, at a processor, a sensor-based position of the autonomous vehicle, which is determined based on data from one or more sensors and based on a digital map. The method also includes determining, at the processor, a global positioning system (GPS) computed position of the autonomous vehicle and a GPS time. The GPS computed position and the GPS time are determined based on a plurality of GPS signals received from GPS satellites. The method further includes comparing the sensor-based position to the GPS computed position to determine whether the sensor-based position is within a threshold distance from the GPS computed position. The method also includes updating the clock associated with the autonomous vehicle based on the GPS time in response to a determination that the sensor-based position is within the threshold distance from the GPS computed position.
G01S 19/25 - Acquisition or tracking of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS
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
G01S 19/39 - Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
G01S 19/45 - Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
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
63.
AUTONOMOUS VEHICLE TRAJECTORY PLANNING FOR ROBUST FALLBACK RESPONSE
The described aspects and implementations enable autonomous vehicle (AV) trajectory planning for robust fallback responses. A method may include periodically receiving updates to a planned trajectory and updates to a fallback trajectory from a planning system of an AV. The planned trajectory may include a trajectory to a planned location of the AV, and the fallback trajectory may include a trajectory to a fallback stopping location in an environment of the AV. The method may include causing the AV to operate according to the updates to the planned trajectory. The method may include, upon determining that a threshold amount of time has passed since receiving a last update to the planned trajectory from the planning system of the AV, autonomously modifying operation of the AV according to a last update of the fallback trajectory received from the planning system of the AV.
An example system includes a light detection and ranging (LIDAR) device that scans a field-of-view defined by a pointing direction of the LIDAR device. The system also includes an actuator that adjusts the pointing direction of the LIDAR device. The system also includes one or more sensors that indicate measurements related to motion of a vehicle associated with the LIDAR device. The system also includes a controller that causes the actuator to adjust the pointing direction of the LIDAR device based on at least the motion of the vehicle indicated by the one or more sensors.
Example embodiments described herein involve a static dome assembly for light detection and ranging (LiDAR) systems. The system may include a base. At least one LiDAR system may be coupled to the base. The at least one LiDAR system may be configured to transmit and receive light having one or more wavelengths. The assembly may further include a housing encompassing the at least one LiDAR system. The housing may be statically coupled to the base. The housing may further include a dome. The dome may include a wall that is transparent to the one or more wavelengths. The wall may also include a high impact polymethyl methacrylate (PMMA) material having an index of refraction of less than 1.55.
Methods and systems are disclosed for cross-validating a second sensor with a first sensor. Cross-validating the second sensor may include obtaining sensor readings from the first sensor and comparing the sensor readings from the first sensor with sensor readings obtained from the second sensor. In particular, the comparison of the sensor readings may include comparing state information about a vehicle detected by the first sensor and the second sensor. In addition, comparing the sensor readings may include obtaining a first image from the first sensor, obtaining a second image from the second sensor, and then comparing various characteristics of the images. One characteristic that may be compared are object labels applied to the vehicle detected by the first and second sensor. The first and second sensors may be different types of sensors.
B60W 50/02 - Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
B60R 1/00 - Optical viewing arrangementsReal-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles
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
G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
The technology provides an interior camera sensing system for self-driving vehicles. The sensor system includes image sensors and infrared illuminators to see the vehicle's cabin and storage areas in all ambient lighting conditions. The system can monitor the vehicle for safety purposes, to detect the cleanliness of the cabin and storage areas, as well as to detect whether packages or other objects have been inadvertently left in the vehicle. The cameras are arranged to focus on selected regions in the vehicle cabin and the system carries out certain actions in response to information evaluated for those regions. The interior space is divided into multiple zones assigned different coverage priorities. Regardless of vehicle size or configuration, certain actions are performed according to various ride checklists and the imagery detected by the interior cameras. The checklists include pre-ride, mid-ride, and post-ride checklists.
Example embodiments relate to range calibration of light detectors. An example method includes emitting a first light signal toward a first region of a calibration target having a first reflectivity and detecting a reflection of the first light signal. The detected reflection of the first light signal has a first intensity. The example method further includes emitting a second light signal toward a second region of the calibration target having a second reflectivity and detecting a reflection of the second light signal from the second region of the calibration target. The detected reflection of the second light signal has a second intensity. Still further, the example method includes determining a first apparent range based on the detected reflection of the first light signal, determining a second apparent range based on the detected reflection of the second light signal, and generating walk-error calibration data for the detector.
Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode. For instance, a current state of a traffic light may be determined. One of a plurality of yellow light durations may be selected based on the current state of the traffic light. When the traffic light will turn red may be predicted based on the selected one. The prediction may be used to control the vehicle in the autonomous driving mode.
Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. For instance, a control command identifying a steering control position may be received by a steering system of the vehicle from an autonomous driving control system. Orientation of one or more wheels of the vehicle may be controlled according to the steering control position. After receiving the control command, that the steering system has not received a valid control command from the control system for a first predetermined duration may be determined. Based on the determination that the steering system has not received a valid control command from the control system for the first predetermined duration, the orientation of the one or more wheels may be continue to be controlled based on the steering control position.
A system for clearing a sensor cover is described. The system includes a first sensor that rotates within a sensor cover, a plurality of second sensors that are fixed relative to the sensor cover, a first wiper that is configured to clear the sensor cover of debris, and a motor. The motor is capable of rotating the first wiper in a first direction at a first predetermined rotation rate defined at least in part by a second predetermined rotation rate of the first sensor.
Aspects of the disclosure relate to providing sensor data on a display of a vehicle. For instance, data points generated by a lidar sensor may be received. The data points may be representative of one or more objects in an external environment of the vehicle. A scene including a representation of the vehicle from a perspective of a virtual camera, a first virtual object corresponding to at least one of the one or more objects, and a second virtual object corresponding to at least one object identified from pre-stored map information may be generated. Supplemental points corresponding to a surface of the at least one object identified from the pre-stored map information may be generated. A pulse including at least some of the data points generated by the sensor and the supplemental points may be generated. The scene may be displayed with the pulse on the display.
The technology involves determining a minimum lateral gap distance between a vehicle configured for autonomous driving and one or more other objects in the vehicle's environment. The minimum lateral gap is used when determining whether to have a driver take over control of certain driving operations. This provides a measure of safety during disengagements or other change of control events. Determining the minimum lateral gap includes calculating a budgeted distance based on a cross-track error for a lateral position of the vehicle, an allowed actual gap distance to an object in the vehicle's environment, and a perception error associated with a location of the object in the vehicle's environment. This determination can be done for a set of possible operating speeds and steering rate limit combinations. The vehicle's control system may notify the driver to take control, for instance with audible, visual and/or haptic notifications.
The disclosure relate to detecting differences indicative of non-determinism in software used to control autonomous vehicles. For instance, a simulation may be run using the software at least two times. The output of the simulations may be tracked. The output of the simulations includes messages may be generated by a decision making module of the software that affect vehicle behavior. A pair of messages generated from each of the at least two times the simulation is run may be compared. Based on the comparison, a difference indicative of non-determinism in the software may be detected.
Example embodiments relate to crosstalk reduction for light detection and ranging (lidar) devices using wavelength locking. An example embodiment includes a lidar device. The lidar device includes a first light emitter configured to emit a first light signal and a second light emitter configured to emit a second light signal. The lidar device also includes a first light guide and a second light guide. In addition, the lidar device includes a first light detector and a second light detector. Further, the lidar device includes a first wavelength-locking mechanism configured to use a portion of the first light signal to maintain a wavelength of the first light signal and a second wavelength-locking mechanism configured to use a portion of the second light signal to maintain a wavelength of the second light signal. The wavelengths of the first light signal and the second light signal are different.
The technology relates to autonomous vehicles suffering a breakdown along a roadway. Onboard systems may utilize various proactive operations to alert specific vehicles or other objects on or near the roadway about the breakdown. This can be done alternatively or in addition to turning on the hazard lights or calling for remote assistance. The disabled vehicle is able to detect nearby and approaching objects. The detection may be performed in combination with a determination of the type of object or predicted behavior for that object, enables the vehicle to generate a targeted alert that can be transmitted or otherwise presented to that particular object. This approach provides the other object, such as a vehicle, bicyclist or pedestrian, sufficient time and information about the breakdown to take appropriate corrective action. Different communication options are available and may be selected based on the particular object, environmental conditions and other factors.
B60Q 1/52 - Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating other intentions or conditions, e.g. request for waiting or overtaking for indicating emergencies
B60Q 9/00 - Arrangement or adaptation of signal devices not provided for in one of main groups
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
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
Sensor data identifying an emergency vehicle approaching the autonomous vehicle may be received. A predicted trajectory for the emergency vehicle may be received. Whether the autonomous vehicle is impeding the emergency vehicle may be determined based on the predicted trajectory and map information identifying a road on which the autonomous vehicle is currently traveling. Based on a determination that the autonomous vehicle is impeding the emergency vehicle, the autonomous vehicle may be controlled in an autonomous driving mode in order to respond to the emergency vehicle.
A method includes sending, by a processing device to a sensor component of a sensing system of an autonomous vehicle (AV), a request to establish a session with the sensor component. The sensor component includes a sensor, a sensor controller and a first cryptographic coprocessor, and the processing device includes a second cryptographic coprocessor operatively coupled to a hardware accelerator. The method further includes determining, by the processing device, whether the sensor component acknowledges the request and, in response to determining that the sensor component acknowledges the request, establishing, by the processing device, the session with the sensor component.
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
The described aspects and implementations enable efficient detection and classification of objects with machine learning models that deploy a bird's-eye view representation and are trained using depth ground truth data. In one implementation, disclosed are system and techniques that include obtaining images, generating, using a first neural network (NN), feature vectors (FVs) and depth distributions pixels of images, wherein the first NN is trained using training images and a depth ground truth data for the training images. The techniques further include obtaining a feature tensor (FT) in view of the FVs and the depth distributions, and processing the obtained FTs, using a second NN, to identify one or more objects depicted in the images.
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 3/4046 - Scaling of whole images or parts thereof, e.g. expanding or contracting using neural networks
G06T 7/55 - Depth or shape recovery from multiple images
G06V 10/40 - Extraction of image or video features
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
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G06V 30/18 - Extraction of features or characteristics of the image
81.
DENIAL OF SERVICE RESPONSE TO THE DETECTION OF ILLICIT SIGNALS ON THE IN-VEHICLE COMMUNICATION NETWORK
Whether an illicit signal transmitted on an in-vehicle communication network of a vehicle satisfies a threshold condition is determined. Responsive to determining that the illicit signal satisfies the threshold condition, a countermeasure operation that renders inoperable communication on at least part of the in-vehicle communication network affected by the illicit signal is determined.
The described aspects and implementations enable fast and accurate verification of radar detection of objects in autonomous vehicle (AV) applications using combined processing of radar data and camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar data characterizing intensity of radar reflections from an environment of the AV, identifying, based on the radar data, a candidate object, obtaining a camera image depicting a region where the candidate object is located, and processing the radar data and the camera image using one or more machine-learning models to obtain a classification measure representing a likelihood that the candidate object is a real object.
G01S 7/41 - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisationTarget signatureTarget cross-section
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G01S 13/86 - Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
G01S 13/931 - Radar or analogous systems, specially adapted for specific applications for anti-collision purposes of land vehicles
The technology uses augmented reality (AR) elements for enhanced wayfinding with autonomous vehicle pickups and drop-offs. The approach includes generating, for presentation in a first region of a client device UI, trip information regarding a trip. Map information associated with the trip is generated for presentation in a second UI region, including at least one of a pickup location for a rider, a walking path from a current location of the rider to the pickup location, a planned route of the vehicle to the pickup location, or a current location of the vehicle. An AR indicator is generated for presentation in the second UI region. Upon selection of the indicator, the system modifies the second region into a first section to display at least a portion of the map information and a second section to display an augmented reality view, or replace the map information with the AR view.
The described aspects and implementations enable efficient calibration of a sensing system of an autonomous vehicle (AV). In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to collect sensing data and a data processing system, operatively coupled to the sensing system. The data processing system is configured to identify reference point(s) in an environment of the AV, determine multiple estimated locations of the reference point(s), and adjust parameters of the sensing system based on a loss function representative of differences of the estimated locations.
The disclosure relate to determining a realism metric for testing software for operating a vehicle in an autonomous driving mode. For instance, a plurality of versions of a simulation may be run using log data collected by a vehicle operating in an autonomous driving mode. The plurality of versions may be run using the software to control a simulated vehicle, and each of the plurality of versions may have a set of timing requirements different from the set of timing requirements of other of the plurality of versions. Results of the plurality of versions may be compared to the log data. A realism metric defining timing requirements for one or more future simulations may be generated based on the comparison.
Aspects of the disclosure relate to arranging trips for an autonomous vehicle transportation service. In one instance, a request for a first trip from a user is received. The trip may include a pickup location and a destination location. Whether the user has previously completed the first trip may be determined by comparing the pickup location and the destination location to a trip history for the user. Based on the determination, a notification suggesting that the user establish a routine may be provided for display to the user. Confirmation that the user wants to establish a routine based on the pickup location and the destination location may be received. The routine may be stored in memory for later use.
A sensor (e.g., an optical sensor) is disposed in an enclosure that separates it from an external environment. The enclosure includes an aperture comprising a transparent material. A laminate comprising a plurality of transparent films is coupled to the aperture such that the sensor has a field of view of the external environment through the transparent material of the aperture and the plurality of transparent films. The laminate includes an exposed transparent film that is exposed to the external environment and that is removable to expose an underlying film in the laminate when dirt, debris, or other obscuring material has collected on it. A laminate module that includes the laminate, a laminate sensor, and a peeling mechanism may be removably mounted on the aperture. The laminate sensor can sense the number of transparent films remaining in the laminate. The peeling mechanism can peel away individual transparent films in the laminate.
A method includes obtaining, by a processing device, input data derived from a set of sensors associated with an autonomous vehicle (AV). The input data includes camera data and radar data. The method further includes extracting, by the processing device from the input data, a plurality of sets of bird's-eye view (BEV) features. Each set of BEV features corresponds to a respective timestep. The method further includes generating, by the processing device from the plurality of sets of BEV features, an object flow for at least one object. Generating the object flow includes performing at least one of: multi-frame temporal aggregation or multi-frame dense motion estimation. The method further includes causing, by the processing device, a driving path of the AV to be modified in view of the object flow.
Example embodiments relate to systems, apparatus, and methods for determining a characteristic of a radome. An example apparatus may comprise a computing device configured to cause a first radar signal to be transmitted toward an object and to receive first radar data representative of a first reflected radar signal. The computing device may also be configured to determine a first parameter associated with the first reflected radar signal based on at least the first radar data and to cause a second radar signal to be transmitted toward the object after a fluid is applied to the radome. Further, the computing device may be configured to receive second radar data representative of a second reflected radar signal, determine a second parameter associated with the second reflected radar signal based on at least the second radar data, and compare the first parameter to the second parameter.
The technology relates to detection of a nearby occluded object based on illumination emitted from that object. Illumination by the occluded object of one or more areas in the surrounding area, for instance by headlights of an occluded vehicle, is detected by a perception system of a self-driving vehicle. The self-driving vehicle can classify the detected object to determine whether the illumination is caused by a vehicle or other road user, or from objects in the surrounding environment. Illumination data and other information can be evaluated by the self-driving vehicle, for instance to identify a type of the object, a location of the object along a roadway, to disambiguate the direction of travel of the other object, etc. As a result, the self-driving vehicle may infer the behavior of the other object and modify its own driving operations to account for the other object's presence and likely behavior
Aspects of the disclosure provide for controlling an autonomous vehicle to enter a driveway. As an example, an instruction to pickup or drop off a passenger at a location may be received. It may be determined that the vehicle is arriving in a lane on an opposite side of a street as the location, the lane having a traffic direction opposite to a traffic direction of a lane adjacent to the location. A difficulty score to maneuver the vehicle to the lane adjacent to the location may be determined, and the difficulty score may be compared to a predetermined difficulty score. Based on the comparison, it may be determined to an available driveway on the same side of the street as the location. The vehicle may be controlled to enter the available driveway.
Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. For instance, a trajectory is identified. The trajectory defines a future desired path for the vehicle and includes a control requirement having a corresponding point in time. A command for achieving the control requirement is generated. A fixed delay value corresponding to a delay caused by transmission of the command to an actuator of the vehicle is generated. A variable delay value corresponding to a delay caused by an amount of time to change a physical state of the actuator to a desired state according to the command is generated. The command is then sent to the actuator based on the fixed delay value, the variable delay value, and so that the actuator causes the vehicle to move according to the command.
Example embodiments relate to controlling gear shifts based on future trajectory information. A vehicle computing system may receive sensor data representing an environment of the vehicle during autonomous navigation of a path and determine, based on the sensor data, the vehicle is approaching a predefined situation positioned along the path. The computing system may then estimate a future acceleration or speed associated with navigation of the predefined situation by the vehicle. The computing system may determine, based on the future acceleration or speed and a current acceleration of the vehicle, whether to perform a gear shift prior to navigation of the predefined situation by the vehicle, and control the vehicle based on determining whether to perform the gear shift prior to navigation of the predefined situation.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
F16H 59/44 - Inputs being a function of speed dependent on machine speed
F16H 59/46 - Inputs being a function of speed dependent on a comparison between speeds
F16H 59/48 - Inputs being a function of acceleration
F16H 59/52 - Inputs being a function of the status of the machine, e.g. position of doors or safety belts dependent on the weight of the machine, e.g. change in weight resulting from passengers boarding a bus
F16H 59/66 - Road conditions, e.g. slope, slippery
F16H 61/00 - Control functions within change-speed- or reversing-gearings for conveying rotary motion
F16H 61/02 - Control functions within change-speed- or reversing-gearings for conveying rotary motion characterised by the signals used
Systems and methods for performing operations based on LIDAR communications are described. An example device may include one or more processors and a memory coupled to the one or more processors. The memory includes instructions that, when executed by the one or more processors, cause the device to receive data associated with a modulated optical signal emitted by a transmitter of a first LIDAR device and received by a receiver of a second LIDAR device coupled to a vehicle and the device, generate a rendering of an environment of the vehicle based on information from one or more LIDAR devices coupled to the vehicle, and update the rendering based on the received data. Updating the rendering includes updating an object rendering of an object in the environment of the vehicle. The instructions further cause the device to provide the updated rendering for display on a display coupled to the vehicle.
A laser diode firing circuit for a light detection and ranging (LIDAR) device that includes an inductively coupled feedback system is disclosed. The firing circuit includes a laser diode coupled in series with a transistor, such that current through the laser diode is controlled by the transistor. The laser diode is configured to emit a pulse of light in response to current flowing through the laser diode. A feedback loop is positioned to be inductively coupled to a current path of the firing circuit that includes the laser diode. As such, a change in current flowing through the laser diode induces a voltage in the feedback loop. A change in voltage across the leads of the feedback loop can be detected and the timing of the voltage change can be used to determine the time that current begins flowing through the laser diode.
One example system for determining the orientation of a rotating platform comprises a rotating platform, a first magnetic field sensor configured to sense a magnetic field pointing in a first direction, a second magnetic field sensor configured to sense a magnetic field pointing in a second direction, wherein the second direction is different than the first direction (e.g., opposite the first direction), and at least one magnet producing a local magnetic field. The first magnetic field sensor, the second magnetic field sensor, and the at least one magnet are positioned relative to the rotating platform such that rotation of the rotating platform causes a change in the orientation of the local magnetic field relative to the first magnetic field sensor and the second magnetic field sensor.
B60R 11/00 - Arrangements for holding or mounting articles, not otherwise provided for
B60R 11/02 - Arrangements for holding or mounting articles, not otherwise provided for for radio sets, television sets, telephones, or the likeArrangement of controls thereof
G01B 7/30 - Measuring arrangements characterised by the use of electric or magnetic techniques for measuring angles or tapersMeasuring arrangements characterised by the use of electric or magnetic techniques for testing the alignment of axes
G01R 33/00 - Arrangements or instruments for measuring magnetic variables
97.
Augmented reality display to preserve user privacy
Aspects of the application relate to protecting the privacy of a user of a dispatching service for driverless vehicles. For example, a client computing device may send a request for a vehicle, the request identifying a location of the client computing device. Location information identifying a current location of a vehicle dispatched to the location of the client computing device is received in response to the request. A camera view is displayed on a display of the client computing device, the camera view corresponding to information in a field of view of a camera of the client computing device. When current location of the vehicle is within the field of view of the camera and a threshold distance of the location of the client device, an indicator is displayed on the display at a location corresponding to the current location of the vehicle.
Example embodiments relate to techniques for using visual language models (VLMs) to provide assistance to vehicles that encounter unexpected issues during navigation. A vehicle computing system may use sensor data to detect an unexpected issue impeding the vehicle from autonomously navigating a path and generate a question based on the unexpected issue. The computing system can then provide the question and a portion of the sensor data used to detect the unexpected issue to a remote computing device. The remote computing device inputs the question and the portion of the sensor data into a VLM trained to answer the question using the portion of the sensor data. The vehicle computing system is able to receive a response from the remote computing system and generate, based on the response, a control strategy for controlling the vehicle.
Aspects of the present disclosure relate generally to systems and methods for assessing validity of a map using image data collected by a laser sensor along a vehicle path. The method may compile image data received from the laser sensor. The map subject to assessment may define an area prohibiting entry by a vehicle.
An article including a durable, optically transparent, and superhydrophobic coating is described. In one aspect, the present disclosure provides an article comprising a substrate, and disposed adjacent the substrate, a layer comprising graphitic carbon, diamond-like carbon, and aerogel. In another aspect, the present disclosure provides a method for preparing a coated substrate, comprising providing a carbon layer disposed on a substrate and having a textured surface; and disposing aerogel adjacent to at least a portion of the textured surface.
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