Abstract: Techniques are described for an autonomous vehicle to provide the autonomous vehicle's health-related information, identification information, and/or inspection information to a device that may be used by law enforcement or a government agency or a third-party so that the autonomous vehicle can comply with government regulations and can continue to operate on the road.

Abstract: Provided herein is a system and method for damping the steering of a vehicle. Methods of an example include: steering a pair of steerable wheels of the vehicle with a steering mechanism about an axis substantially perpendicular to an axis of rotation of a respective one of the pair of steerable wheels; damping the steering mechanism at a first damping rate in response to a steering change rate of the pair of steerable wheels being below a predetermined rate; and damping the steering mechanism at a second damping rate in response to the steering change rate of the pair of steerable wheels being above the predetermined rate.

Abstract: A telescopic sensor system for an autonomous vehicle enables sensors located on movable telescopic apparatuses to obtain sensor data when an object obstructs an area scanned by fixed sensors. An example method of controlling a movable telescopic apparatus on an autonomous vehicle includes obtaining, from a first sensor located on the autonomous vehicle, a first sensor data of a first area relative to a location of the autonomous vehicle, performing, from the first sensor data, a first determination that a view of the first area is obstructed, causing, in response to the first determination, a second sensor coupled to the movable telescopic apparatus to extend to a pre-determined position, and obtaining, from the second sensor, a second sensor data of a second area relative to the location of the autonomous vehicle, where the second area includes at least some of the first area.

Abstract: A unified framework for detecting perception anomalies in autonomous driving systems is described. The perception anomaly detection framework takes an input image from a camera in or on a vehicle and identifies anomalies as belonging to one of three categories. Lens anomalies are associated with poor sensor conditions, such as water, dirt, or overexposure. Environment anomalies are associated with unfamiliar changes to an environment. Finally, object anomalies are associated with unknown objects. After perception anomalies are detected, the results are sent downstream to cause a behavior change of the vehicle.

Abstract: Devices, systems, and methods for power distribution in autonomous vehicles are described. An example method of power distribution includes receiving, by a controller disposed in a vehicle, an indication of a fault associated with a load unit of a plurality of load units, wherein the load unit is communicatively coupled to an associated solid-state semiconductor switch, disconnecting, in response to the receiving the indication of the fault, the associated solid-state semiconductor switch, and transmitting, using a controller area network bus in the vehicle communicatively coupled the controller, a digital data message indicative of disconnecting the associated solid-state semiconductor switch to a vehicle control unit.

Abstract: Autonomous vehicles can include systems and apparatus for performing signal processing on point cloud data from Light Detection and Ranging (LiDAR) devices located on the autonomous vehicles. A method includes obtaining, by a computer located in an autonomous vehicle, a combined point cloud data that describes a plurality of areas of an environment in which the autonomous vehicle is operating; determining that a first set of points from the combined point cloud data are located within fields of view of cameras located on the autonomous vehicle; assigning one or more labels to a second set of points from the first set of points in response to determining that the second set of points are located within bounding box(es) around object(s) in images obtained from the cameras; and causing the autonomous vehicle to operate based on characteristic(s) of the object(s) determined from the second set of points.

Abstract: A system comprises an autonomous vehicle and a control device associated with the autonomous vehicle. The autonomous vehicle comprises sensors. The control device accesses sensor data captured by the sensors. The control device identifies a vehicle of interest from the sensor data. The control device communicates information associated with the vehicle of interest to at least one of an oversight server and a third party.

Abstract: Devices, systems, and methods for predictive anti-lag braking control for autonomous vehicles are described. An example method of controlling a vehicle includes receiving, from a controller, a first plurality of values indicative of a brake wheel torque demand associated with a deceleration profile of the vehicle, deriving, based on the first plurality of values and a steady-state brake model, a second plurality of values indicative of a brake pressure demand, wherein an input to the steady-state brake model comprises the brake pressure demand and at least one previous brake wheel torque demand, and wherein an output of the steady-state brake system comprises a current brake wheel torque demand, generating, based on the second plurality of values and at least the steady-state model, a plurality of brake control commands, and controlling, using the plurality of brake control commands, the air brake system.

Abstract: A redundant hardware and software architecture can be designed to enable vehicles to be operated in an autonomous mode while improving the reliability and/or safety of such vehicles. A system for redundant architecture can include a set of at least two redundant sensors coupled to a vehicle and configured to provide timestamped sensor data to each of a plurality of computing unit (CU) computers. The CU computers can process the sensor data simultaneously based on at least a time value indicative of an absolute time or a relative time and based on the timestamped sensor data. The CU computers provide to a vehicle control unit (VCU) computer at least two sets of outputs configured to instruct a plurality of devices in a vehicle and cause the vehicle to be driven.

Abstract: An autonomous vehicle includes an under-chassis object detection system for identifying the presence of an object on a road that the autonomous vehicle is travelling upon. The under-chassis object detection system may include a LIDAR system. The object on the road that the autonomous vehicle is travelling upon is of a size that allows the vehicle's chassis to pass over the object on the road. The autonomous vehicle may react to the detected object on the road to operate the autonomous vehicle safely, such as by altering the vehicle's trajectory, by stopping the vehicle, or by communicating with a control center for further instructions.

Abstract: A system and method for predicting non-operational design domain (ODD) scenarios are disclosed. In one aspect, a server includes a network communication device configured to communicate with an autonomous vehicle over a network, a memory, and a processor configured to receive information related to the navigation of the autonomous vehicle from a plurality of data sources. The processor is further configured to predict whether the autonomous vehicle will encounter a non-ODD scenario based on the received information and transmit a signal to the autonomous vehicle in response to predicting that the autonomous vehicle will encounter the non-ODD scenario.

Abstract: An example method includes detecting, via sensor data collected from sensors located on the AV, an upcoming object located on a roadway. The method further includes determining, from the sensor data, a relative distance and a relative direction of the upcoming object with respect to the autonomous vehicle. The method further includes mapping the upcoming object to an absolute location with respect to the roadway based on map data that describes upcoming topology of the roadway and a location of the autonomous vehicle. The method further includes associating the upcoming object with a lane of the roadway based on the absolute location mapped to the upcoming object and based on lane geometry data for the roadway. The method further includes operating the autonomous vehicle based on a relationship between the lane associated with the upcoming object and a current lane in which the autonomous vehicle is located.

Abstract: A system, method and computer program product control data communication based on a characteristic of an exhaust gas from a vehicle. In the context of a system, an exhaust snorkel is in fluid communication with an exhaust system of the vehicle and configured to direct the exhaust gas from the vehicle to a ventilation system. The system also includes a data transmission system configured to provide for data communication between the vehicle and an offboard computer system. The system further includes a sensor system configured to control operation of the data transmission system based on the characteristic of the exhaust gas.

Abstract: Techniques are described for determining visibility conditions of an environment in which an autonomous vehicle is operated and performing driving related operations based on the visibility conditions. An example method of adjusting driving related operations of a vehicle includes determining, by a computer located in an autonomous vehicle, a visibility related condition of an environment in which the autonomous vehicle is operating, adjusting, based at least on the visibility related condition, a set of one or more values of one or more variables associated with a driving related operation of the autonomous vehicle, and causing the autonomous vehicle to be driven to a destination by causing the driving related operation of one or more devices located in the autonomous vehicle based on at least the set of one or more values.

Abstract: A system and method for online real-time multi-object tracking is disclosed. A particular embodiment can be configured to: receive image frame data from at least one camera associated with an autonomous vehicle; generate similarity data corresponding to a similarity between object data in a previous image frame compared with object detection results from a current image frame; use the similarity data to generate data association results corresponding to a best matching between the object data in the previous image frame and the object detection results from the current image frame; cause state transitions in finite state machines for each object according to the data association results; and provide as an output object tracking output data corresponding to the states of the finite state machines for each object.

Abstract: A system for implementing a lane bias maneuver to negotiate a curved road comprises an autonomous vehicle and a control device. The control device determines that the autonomous vehicle is approaching a curved road. The control device determines a road radius of the curved road. The control device calculates a first lane bias adjustment amount associated with a road curvature of the curved road based on the road radius. The control device calculates a second lane bias adjustment amount associated with a trailer angle between a trailer and a semi-truck tractor unit of the autonomous vehicle. The control device calculates a total lane bias adjustment amount by combining the first and second lane bias adjustment amounts. The control device instructs the autonomous vehicle to perform a lane bias maneuver that comprises driving the autonomous vehicle off-center in a curved lane based on the total lane bias adjustment amount.

Abstract: Devices, systems, and methods for estimating wheel torque in an autonomous vehicle include a method for controlling a vehicle includes using a dual filter framework to generate an updated estimate of a wheel torque, and controlling, based on the updated estimate of the wheel torque, the vehicle. A first filter of the dual filter framework is configured to generate a blended estimate of the wheel torque by combining a first estimate of the wheel torque that is generated based on a set of mechanical torque transfer models of at least a powertrain and a brake of the vehicle and a second estimate of wheel torque that is generated based on an inverted longitudinal dynamic model, and a second non-linear filter of the dual filter framework is configured to generate the updated estimate of a wheel torque and vehicle velocity based on a torque delivery damping process model on vehicle motion.

Abstract: Devices, systems and methods for operating a rear-facing perception system for vehicles are described. An exemplary rear-facing perception system contains two corner units and a center unit, with each of the two corner units and the center unit including a camera module and a dual-band transceiver. A method for operating the rear-facing perception system includes pairing with a control unit by communicating, using the dual-band transceiver, over at least a first frequency band, transmitting a first trigger signal to the two corner units over a second frequency band non-overlapping with the first frequency band, and switching to an active mode. In an example, the first trigger signal causes the two corner units to switch to the active mode, which includes orienting the camera modules on the center unit and the two corner units to provide an unobstructed view of an area around a rear of the vehicle.

Abstract: An autonomous vehicle detects an approaching emergency vehicle by performing a plurality of types of analysis of signals output by sensors in the vehicle. Each type of analysis generates a different prediction that indicates a likelihood of a proximity of an emergency vehicle to the autonomous vehicle. The predictions are processed and fused to generate a determination that indicates whether an emergency vehicle is proximate to the autonomous vehicle. In response to the determination indicating the emergency vehicle is proximate, the autonomous vehicle performs an action.

Abstract: An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example method for operating an AV includes receiving, by a computer located in the AV, sensor data that indicates a condition of one or more devices in the AV or of a roadway on which the autonomous vehicle is operating; and causing, by the computer and based on the sensor data, the autonomous vehicle to operate in accordance with a maneuver by sending instructions to one or more devices in the autonomous vehicle, wherein the maneuver is configured to bring the autonomous vehicle to a complete stop. In some embodiments, the maneuver may be selected from a plurality of maneuvers in response to determining an occurrence of a fault condition based on the sensor data.