Abstract: Examples of the present disclosure provide a computer-implemented system, comprising instructions for performing operations including: estimating locations in a region of a map where features associated with water, snow or ice are likely to be formed under certain adverse weather conditions; simulating one of the adverse weather conditions; generating the features associated with the simulated one of the adverse weather conditions at the locations; determining a response of a perception stack of an autonomous vehicle (AV) to the adverse weather conditions observed at the locations; determining a reaction of the AV to the features generated in the region, the reaction of the AV being a function of a configuration of the AV; in response to the reaction, updating the configuration; repeating the determining the reaction and the updating the configuration until a desired reaction is obtained; and exporting a final configuration corresponding to the desired reaction to a physical AV.

Abstract: Systems and methods for providing kinematic and contextual comfort scores to autonomous vehicle rides. In particular, systems and methods are provided to automatically identify driving events such as unusual acceleration, braking, and steering events, and determine the severity of identified events. Some example events include a hard braking event, a swerve event, and a quick acceleration event. Automatic detection of such events allows for engineering feedback to autonomous vehicles and provides a tool for capturing passenger satisfaction and/or reactions with regard to specific events.

Abstract: Particular embodiments described herein provide for a system and method to help enable a towing vehicle. In an example, the system can identify a location of the disabled vehicle, deploy the towing vehicle to the location of the disabled vehicle, where the towing vehicle uses sensor data from a sensor suite and a perception module to navigate to the location of the disabled vehicle, and use the towing vehicle to tow the disabled vehicle to a new location.

Abstract: Systems and method for updating vehicles during transport. In particular, systems and methods are provided for transport vehicles to provide secure wireless updates to autonomous vehicles being transported. By updating vehicles during transport, significant time savings can be achieved, as vehicles can arrive at a destination ready for deployment. A secure protocol can be used by the transport vehicle for the updates to protect software and other data.

Abstract: The disclosed technology provides solutions for vehicle routing and in particular, for facilitating obstacle avoidance maneuvering by an autonomous vehicle (AV). In some implementations, a process of the disclosed technology can include steps for collecting environmental data about an environment around an autonomous vehicle, wherein the environmental data comprises data pertaining to a roadway navigated by the autonomous vehicle and one or more other vehicles navigating the roadway, processing the environmental data to generate an area grid comprising one or more grid sections, wherein each grid section corresponds with a different area of the roadway, and determining a risk-profile for the one or more grid sections based on the environmental data. Systems and machine-readable media are also provided.

Abstract: Systems and methods for vehicle hardware coverage testing are provided. For instance, a vehicle comprises a memory to store a vehicle compute software and a hardware coverage test software associated with the vehicle compute software; a timer configured based on a timer period; and one or more processing units to execute the vehicle compute software using hardware resources of the one or more processing units; and responsive to an expiration of the timer, execute the hardware coverage test software to test only the hardware resources used by the vehicle compute software and determine whether there is a failure associated with the hardware resources used by the vehicle compute software.

Abstract: The subject disclosure relates to techniques for detecting an object. A process of the disclosed technology can include steps for receiving three-dimensional (3D) Light Detection and Ranging (LiDAR) data of the object at a first time, generating a first point cloud based on the 3D LiDAR data at the first time, receiving 3D LiDAR data of the object at a second time, generating a second point cloud based on the 3D LiDAR data at the second time, aggregating the first point cloud and the second point cloud to form an aggregated point cloud, and placing a bounding box around the aggregated point cloud. Systems and machine-readable media are also provided.

Abstract: The present technology provides a cargo adapter that can be inserted into a passenger compartment of a mixed-use vehicle that can be anchored to existing anchor points in the vehicle. The cargo adapter can then receive, secure, and support a cargo locker in a way that safely secures the cargo and avoids wear on the interior surfaces of the passenger compartment. The present technology further provides a system for transporting a cargo locker to a vehicle and inserting the cargo adapter and cargo locker into the vehicle quickly and easily.

Abstract: Embodiments may relate to a vehicle comprising: a sensor to identify, based on an audio or visual condition, the occurrence of a condition related to a passenger of the vehicle. The vehicle may further include a processor coupled with the sensor. The processor may be configured to identify, in a user profile of the first passenger of the vehicle, a pre-identified action. The processor may further be configured to perform, or facilitate the performance of, the pre-identified action by the vehicle based on the occurrence of the audio or visual condition. Other embodiments may be described or claimed.

Abstract: The subject disclosure relates to techniques for application-based controls of a wheelchair-accessible autonomous vehicle. A process of the disclosed technology can include steps for receiving a request for a wheelchair-accessible autonomous vehicle (WAV) to execute an ingress function, wherein the ingress function includes a deployment of a wheelchair ramp by the WAV, sending an ingress command to the WAV in response to the request, wherein the ingress command comprises instructions cause the WAV to execute the ingress function at a specified pick-up location, and receiving feedback from the WAV, the feedback includes sensor data associated with a wheelchair ramp and status information associated with the ingress function. Machine-readable media and systems are also provided.

Abstract: A system for mitigating thermal runaway in a battery-powered electric vehicle (EV). The system includes a gas sensor configured to measure a level of at least one type of gas in a vicinity of a battery of the EV, a thermal event detector configured to determine, based on the measured level of the at least one type of gas, that the battery is experiencing out-gassing, and a communications interface configured to transmit an alert to a fleet management system regarding the out-gassing of the battery. The fleet management system alters an assignment of the EV in response to the out-gassing of the battery.

Abstract: Systems and methods for a selectively-activated magnetic strip under a vehicle chassis that is powered to pick up nails and other metal debris. The magnetic strip can be selectively activated using an electromagnet powered by the vehicle and controlled by an onboard computer. The magnetic strip can generally be powered on while the vehicle is driving and selectively deactivated based on mapping data to avoid interactions with metallic infrastructure. The magnetic strip can be selectively deactivated based on vehicle parameters, such as vehicle speed.

Abstract: Disclosed are systems and methods for scintillation-based neural network for RADAR target classification. In some aspects, a method includes generating a point cloud from radio frequency (RF) scene responses received from a radio detection and ranging (RADAR) sensor for a scanned scene; populating a rolling buffer with frame data from the point cloud, the frame data including radar cross section (RCS) values, RCS scintillation measurements, and velocity values for objects in the scanned scene; inputting the RCS scintillation measurements and velocity values for an object of the objects to a convolutional neural network (CNN); and receiving a classification of the object from the CNN, wherein the CNN is to utilize a probability density function (PDF) estimate of the RCS scintillation measurements and the velocity values to determine fits with one or more reference PDFs based on a Neyman-Pearson evaluation, and wherein the fits are assessed to classify the object.

Abstract: Aspects of the disclosed technology encompass solutions for automatically requesting a backup vehicle for passengers of an autonomous vehicle provisioned ride-hailing service. In some aspects, a process of the disclosed technology includes steps for collecting diagnostic data relating to at least one AV operation, and analyzing the diagnostic data to determine if the AV needs maintenance. Moreover, in response to a determination that the AV needs maintenance, the process can include steps for automatically requesting a backup service for a passenger of the AV. Systems and machine-readable media are also provided.

Abstract: Curvelet-based low level fusion of camera and RADAR sensor information is disclosed and includes processing RADAR data corresponding to a scene to generate a RADAR point cloud and processing camera image data corresponding to the scene using a curvelet transform to identify a target of interest in the scene and generate for the target of interest a target type, (x,y) coordinate values, and a curvelet magnitude per decomposition level. If discrepancies exist between (x,y) coordinate values of the RADAR point cloud and the target type, (x,y) coordinate values, and curvelet magnitude per composition level of the target of interest, a portion of the RADAR data processing is repeated to regenerate the RADAR point cloud; otherwise, the RADAR point cloud to a perception stack of a vehicle.

Abstract: Disclosed are systems and methods for synthetic loading of configurable logic devices. In some aspects, a method includes determining computational elements that are available for configuration in a configurable logic hardware device; executing a compilation process to program the available computational elements with a conditionally-enabled configuration and to program one or more load enable trees to enable a load of the conditionally-enabled configuration at the computational elements; configuring a software interface to enable the load of the conditionally-enabled configuration on portions of the computational elements via the one or more load enable trees based on a percentage enablement parameter; and enabling iteration through different percentage enablement parameter values, via the software interface, to cause the load of the conditionally-enabled configuration to be applied to different portions of the computational elements without recompiling the configurable logic hardware device.

Abstract: Disclosed are systems and methods for detecting weather conditions at a LiDAR sensor level. In some aspects, a method includes calculating a reference probability mass function (PMF) of at least one field of a point cloud generated from reference scene responses received from a light detection and ranging (LiDAR) sensor; calculating a current PMF for the at least one field of the point cloud generated from a current scene response received from the LiDAR sensor; determining a statistical difference between the reference PMF and the current PMF using a Kullbeck-Leibler (KL) divergence calculation; and responsive to the statistical difference satisfying a threshold for the at least one field, flagging an environmental change in the current scene response.

Abstract: Disclosed are systems and methods for a sensor synchronization system. In some aspects, a method includes synchronizing sensors of an autonomous vehicle (AV) to cause sampling of a first sensor of the sensors to occur in alignment with sampling of a second sensor of the sensors; determining a sampling time point for the first sensor based on the synchronizing the sensors, the sampling time point comprising a time when the first sensor is in alignment with the second sensor; providing the sampling time point to the first sensor; determining, at a controller circuit of the first sensor, an offset of the first sensor to apply to a local system time of the first sensor to cause a data acquisition of the first sensor to occur at the sampling time point; and causing the data acquisition to be performed at the first sensor using the sampling time point and the offset.

Abstract: There is disclosed a method of operating an autonomous vehicle (AV), including: detecting an alert condition within the AV via data from an electronic sensor suite of the AV; based at least in part on detecting the alert condition, automatically initiating a call with another party; and communicatively coupling a passenger of the AV to the conference call, whereby the passenger can communicate with the other party via the conference call.

Abstract: For some embodiments of the present disclosure, systems and methods for using computer vision to guide processing of receive responses of RADAR sensors of a vehicle are described. A computer implemented method comprises receiving, with one or more receivers of RADAR sensors, environment receive RF responses, receiving region of interest (ROI) information including segments of a tentative set of ROIs with free space area boundaries from an image processor of an image processing chain for the computer vision, dynamically determining a number of polyphase filters based on a number of ROIs having dynamic scenes that are provided by the image processing chain, and adjusting parameters of the polyphase filters of a RADAR processing chain based on the ROI information.