Abstract: Disclosed herein are systems and methods for operating a vehicle. In an embodiment, a system can identify a maximum distance bound based on one or more objects around a vehicle; for each of a plurality of candidate trajectories for the vehicle, determine a velocity from the maximum distance bound at an ending time of the candidate trajectory; determine an available distance for the candidate trajectory as a function of the determined velocity at the ending time of the candidate trajectory and a comfort deceleration parameter; determine a target velocity for the candidate trajectory; and determine a velocity difference between the target velocity and a final velocity of the candidate trajectory at the ending time of the candidate trajectory; select a first candidate trajectory based on the velocity difference; and operate the vehicle based on the selected first candidate trajectory.

Abstract: An autonomous vehicle can include first one or more sensors located on a first side of the autonomous vehicle; second one or more sensors located on a second side of the autonomous vehicle; and one or more processors. The can processors be configured to automatically control, using images generated by the first one or more sensors, the autonomous vehicle to an energy supply station responsive to determining to resupply energy to the autonomous vehicle; detect, using the images generated by the first one or more sensors, an arrival of the autonomous vehicle at the energy supply station; and responsive to detecting the arrival of the autonomous vehicle at the energy supply station, switch from processing images generated by the first one or more sensors to processing images generated by the second one or more sensors.

Abstract: Disclosed herein are systems and methods for operating a vehicle. In an embodiment, a system can identify a maximum distance bound based on one or more objects around a vehicle; for each of a plurality of candidate trajectories for the vehicle, determine a velocity from the maximum distance bound at an ending time of the candidate trajectory; determine an available distance for the candidate trajectory as a function of the determined velocity at the ending time of the candidate trajectory and a comfort deceleration parameter; determine a target velocity for the candidate trajectory; and determine a velocity difference between the target velocity and a final velocity of the candidate trajectory at the ending time of the candidate trajectory; select a first candidate trajectory based on the velocity difference; and operate the vehicle based on the selected first candidate trajectory.

Abstract: A gap evaluation system for evaluating potential gaps for lane change operations of an autonomous vehicle, wherein the system is configured to receive, from the plurality of sensors, sensor data corresponding to a plurality of potential gaps, transmit sensor data to the at least one processor, generate a profile for each potential gap using the sensor data, retrieve a plurality of gap rules from the at least one memory, wherein each gap rule is directed to a characteristic of a potential gap and wherein each gap rule comprises a threshold value corresponding to the characteristic, evaluate each gap against the plurality of gap rules by comparing the profile for each potential gap to the threshold value of each gap rule, and generate a prioritized list of gaps based on the evaluation of the plurality of potential gaps.

Abstract: A sensor calibration area includes a calibration track extending over a longitudinal length and on which an autonomous vehicle can operate; a first side strip extending longitudinally on a first side of the calibration track; a second side strip extending longitudinally on a second side of the calibration track; a first set of calibration boards and a second set of calibration boards positioned on the first side strip and the second side strip, respectively; an autonomous vehicle detector circuit configured to detect presence of the autonomous vehicle and generate a signal indicating the presence of the autonomous vehicle; a sensor to determine a type of the autonomous vehicle; and a calibration board selector circuit configured to activate at least one calibration board of the first set of calibration boards or the second set of calibration boards for calibration of a plurality of perception sensors mounted on the autonomous vehicle.

Abstract: A method comprises identifying a set of image data captured by at least one autonomous vehicle when the at least one autonomous vehicle was positioned in a lane of a roadway, and respective ground truth localization data of the at least one autonomous vehicle; determining a total number of lanes for the roadway; labeling the set of image data with the total number of lanes for the roadway; and training using the labeled set of image data, a machine learning model, such that the machine learning model is configured to predict a new total number of lanes for a new roadway as output.

Abstract: Systems and techniques are described herein for motion planning for autonomous vehicles. For example, a computing device can generate a hash map including keys associated with time steps and values associated with a nested hash map, the nested hash map including one or more lane coordinates and a list of trajectory constraints associated with the one or more lane coordinates; receive a plurality of candidate trajectories associated with a vehicle; query the hash map to determine violations of the list of trajectory constraints by one or more candidate trajectories of the plurality of candidate trajectories; and select a candidate trajectory from the plurality of candidate trajectories based on the violations of the list of trajectory constraints.

Abstract: An autonomous vehicle selectively couplable to a trailer includes at least one sensor configured to capture data of an environment in which the autonomous vehicle operates and an autonomy computing system operably coupled to the at least one sensor. The autonomy computing system includes at least one memory device in communication with at least one processor programmed to receive the sensor data from the at least one sensor as the autonomous vehicle is in motion, determine velocity of objects in proximity to the autonomous vehicle based on the sensor data, identify one or more rotating objects among the objects in proximity to the autonomous vehicle as wheels of the trailer coupled to the autonomous vehicle based on the velocity, determine a wheelbase of the autonomous vehicle coupled to the trailer based on the wheels, and control operation of the autonomous vehicle coupled to the trailer based on the wheelbase.

Abstract: An autonomous vehicle selectively coupled to a load includes a sensor configured to capture signals of an autonomous vehicle and an autonomy computing system operably coupled to the sensor including a memory device and a processor programmed to receive the signals from the sensor as the autonomous vehicle coupled to the load is executing a driving maneuver, determine, using the received signals, a center of mass defined by the load, determine, based on the center of mass, a weight-on-axle for each axle of a plurality of axles supporting the vehicle and the load, determine if the weight-on-axle for each axle exceeds predetermined maximum weight-on-axle values to determine if the center of mass requires adjustment, and issue a recommendation for adjusting the center of mass to cause the weight-on-axle for each axle to be less than the predetermined maximum weight-on-axle values before weighing the vehicle and the load on a scale.

Abstract: A system for localization of an autonomous vehicle includes a plurality of radio receivers configured to receive automatic dependent surveillance-broadcast (ADS-B) radio signals transmitted by a plurality of overhead aircraft, and at least one processor coupled to at least one memory and configured to perform operations comprising: based upon a first, a second, and a third ADS-B radio signals transmitted by a first, a second, and a third overhead aircraft, respectively, computing an angle from, and a distance between, a radio receiver to a respective overhead aircraft, and computing a relative motion of the respective overhead aircraft; and based upon position information included in the first, second, and third ADS-B radio signals, and using the first angle, second angle, third angle, first distance, second distance, third distance, the relative motion of each of the first, second, and third overhead aircraft, performing multilateration to determine a position of the autonomous vehicle.

Abstract: A method and vehicle for external actor acknowledgment for automated vehicles is disclosed. In an example, a method comprises determining, by a processor, to control a vehicle to switch into a lane adjacent to the vehicle; monitoring, by the processor, a speed or a location of a second vehicle, the second vehicle in the lane adjacent to the vehicle; determining, by the processor, the speed or the location of the second vehicle satisfies a condition; controlling, by the processor, the vehicle to switch into the lane adjacent to the vehicle; and activating, by the processor, an acknowledgment sequence responsive to determining the vehicle has switched into the lane.

Abstract: The present disclosure relates to a method for controlling an autonomously driving vehicle, in particular a commercial vehicle, in which the wheels of the vehicle are monitored. Upon detection of a safety-critical condition of the wheels, the driving behavior of the vehicle is automatically adjusted. In a method in which the vehicle is secured in the event of a tire fire, a fire alarm is sent to an emergency call center external to the vehicle upon identification of a burning tire, and the vehicle changes from the current lane to a shoulder on which the speed is reduced to wear off the burning tire. After the burning tire has been removed from the vehicle, the vehicle is brought to a stop at a safe distance from the burning tire.

Abstract: An autonomy computing system for an autonomous vehicle traveling through a lane width irregularity includes at least one memory storing executable instructions defining a lane positioning module and at least one processor. The processor, upon executing the lane positioning module, is configured to: detect the lane width irregularity; select a lane marking to be used as a guide through the lane width irregularity; and maintain a predefined distance from the lane marking through the lane width irregularity.

Abstract: An autonomy computing system is configured to receive a first temperature measurement at a first time t1 and a second temperature measurement at a second time t2 measured proximate a lens surface for a camera sensor using a first temperature sensor; receive a third temperature measurement at the first time t1 and a fourth temperature measurement at the second time t2 measured using a second temperature sensor for ambient air of a vehicle; compute a first change of temperature between the second temperature measurement and the first temperature measurement, and a second change of temperature between the fourth temperature measurement and the third temperature measurement; and in response to determining that the first change of temperature is greater than the second change of temperature by a predetermined threshold value, activate a heating element in proximity of the camera sensor to reduce presence of a liquid particle at the lens surface.

Abstract: A system for cleaning a vehicle sensor includes a cleaning device movable relative to a vehicle, an actuator configured to position the cleaning device relative to the vehicle, and a sensor configured to collect information relating to a position of a vehicle sensor on the vehicle. The system also includes a controller communicatively coupled to the sensor and the actuator. The controller is configured to receive information from the sensor and identify the vehicle sensor on the vehicle. The controller is configured to operate the actuator to selectively position the cleaning device relative to the vehicle and target the vehicle sensor on the vehicle.

Abstract: A system for decoding information is provided. The system includes a road defining a surface, and an encoded strip formed in the surface of the road or attached to the surface of the road. The encoded strip includes a set of peaks and/or valleys having at least one of an amplitude or a frequency selected to encode information. The system includes one or more sensors associated with a vehicle. The system includes a processing device in communication with the one or more sensors. The processing device is configured to execute instructions stored in a memory to perform operations including, as a first tire of the vehicle passes over the encoded strip, detecting a characteristic of the encoded strip with the one or more sensors. The operations include decoding the information from the encoded strip based on the detected characteristic.

Abstract: A system for intersection operation is provided. The system includes a roadside unit associated with an intersection including a traffic signal. The system includes at least one sensor configured to be located on a vehicle. The system includes a processing device in communication with the roadside unit and the at least one sensor. The processing device is configured to execute instructions stored in a memory to perform operations that include detecting failure or improper operation of the at least one sensor indicative of a degraded operation mode of the vehicle. The operations include communicating to the roadside unit the degraded operation mode of the vehicle. The operations include causing the roadside unit to permit coordinated travel of the vehicle through the intersection.

Abstract: A system including an internal sensor configured to generate first sensor data, an external perception sensor configured to generate second sensor data, at least one memory configured to store computer executable instructions, and at least one processor is disclosed. The at least one processor is coupled to the internal sensor, the external perception sensor, and the at least one memory. The at least one processor is configured to execute the computer executable instructions to: (i) compute a first velocity output based upon the first sensor data; (ii) compute a second velocity output based upon the second sensor data; (iii) compute a relative velocity between the first velocity output and the second velocity output; (iv) determine the relative velocity is outside a predetermined threshold value; and (v) perform an intervening action in response to the determining.

Abstract: A system and method for enabling asymmetric computing redundancy are provided. The system includes a plurality of computing systems with an autonomy system, each comprising a memory device and a processor. The processors write state data to their respective state memory devices and receive data from the autonomy system. An auxiliary computing system functions as a monitor module or sensor suite processor that detects conditions such as processor malfunctions, auxiliary computing failures, or data corruption in one of the plurality of computing systems. In response, it deploys the state data to the auxiliary computing system and reconnects the affected system to the auxiliary system. Some embodiments include a separate program memory device for storing program data, which can be loaded onto the auxiliary computing system. The processors may be implemented as FPGAs or microcontrollers, and one of them could serve as the auxiliary computing system.

Abstract: An autonomous vehicle is provided. The autonomous vehicle comprises sensor(s) configured to detect data relating to an environment surrounding the autonomous vehicle and object(s) included within the environment. The autonomous vehicle also includes an autonomy computing system(s) in communication with the sensor(s). The autonomy computing system(s) include a processor(s) in communication with a memory device(s), and the processor(s) is programmed to calculate a drivable space consumption (DSC) ratio for the autonomous vehicle traveling within the environment based on an anticipated spatial occupancy for the autonomous vehicle within the environment and a drivable space within the environment.