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.

Abstract: An autonomous vehicle is disclosed. The autonomous vehicle includes a plurality of wheels for supporting the autonomous vehicle and a cabin. The cabin is supported by a vehicle frame. The plurality of wheels is attached to connected to the vehicle frame. The cabin has a cabin bottom, a cabin top and a front portion that extends between the cabin bottom and the cabin top. The front portion includes an airfoil that has a substantially rigid front surface and an aerodynamic shape.

Abstract: A method executed by a computing device of an autonomous vehicle includes (i) receiving first sensor data from a network of one or more sensors; (ii) generating a first prediction, based at least in part on the received first sensor data, for an object being associated with a first state with a first confidence value; (iii) receiving second sensor data from the network of one or more sensors after elapsing of time t after receiving the first sensor data; (iv) generating a second prediction, based at least in part on the received second sensor data, for the object being associated with a second state with a second confidence value; and (v) updating a state of the object in a hierarchical representation tree to be the first state or the second state based upon a higher confidence value between the first confidence value and the second confidence value.

Abstract: An appendage disconnection system for an autonomous vehicle includes a plug coupled to a cable, a sleeve encasing the plug, and a plug position control mechanism. The plug is removably attached to a trailer and the plug is attached to a tractor portion of the autonomous vehicle, the plug moveable between a connected position and a disconnected position. The position control mechanism is housed within a body of the plug and is adapted to impede and enable movement of the plug relative to the sleeve.

Abstract: The present disclosure relates to a device for monitoring a lighting unit which has a plurality of lighting sub-units, wherein for each lighting sub-unit or for each group of lighting sub-units includes a monitoring unit with a detector, which is configured to monitor a light output of the lighting sub-unit or group. The detector is further configured to monitor a trigger signal for controlling the lighting sub-unit or group and a power consumption of the lighting sub-unit or group, wherein a monitoring collector unit is arranged and coupled to all monitoring units in order to determine a total output from the data recorded by them.

Abstract: A method comprises identifying a set of radar 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; filtering one or more objects identified via the set of radar data in accordance with an attribute of the one or more objects; generating a map layer to be included within a high definition map; identifying second set of radar data captured by a second autonomous vehicle when the at least one autonomous vehicle was positioned in the lane of the roadway, and respective ground truth localization data of the at least one autonomous vehicle; and localizing the second autonomous vehicle by executing a matching protocol to match an object within the second set of radar data with an object within the map layer.

Abstract: Systems for adjusting travel patterns for autonomous vehicles are disclosed. The system includes a plurality of sensor nodes positioned on a roadway, where each sensor node is operably coupled to at least one distinct sensor node. The system also includes an autonomous vehicle (AV) computing device(s) in electronic communication with the plurality of sensor nodes. The AV computing device(s) adjusts travel patterns for an autonomous vehicle on the roadway by detecting a first sensor node of the plurality of sensor nodes and obtaining positional data for the first sensor node and each subsequent sensor node. The computing device(s) then generate a modified travel pattern for the autonomous vehicle based on positional data for each sensor node, and/or accessibility status for each sensor node. Additionally, the computing device adjusts an initial travel pattern of the autonomous vehicle on the roadway to the modified travel pattern.

Abstract: The system related to a gated camera stereo system, a red-clear-clear-blue (RCCB) camera, at least one memory, and at least one processor communicatively coupled with the at least one memory. The processor is configured to: control the gated camera stereo system to emit a pulse of light at a predetermined wavelength, control a detector of the gated camera stereo system to capture light reflected from a scene after a preset time delay, The processor embeds depth data into two-dimensional gated images using a gate function based upon the captured light by the detector of the gated camera stereo system, control the RCCB camera to capture passive high dynamic range (HDR) images, extract features corresponding to the gated images and the passive HDR images, and fuse the extracted features corresponding to the gated images and the passive HDR images by cross-spectral matching for depth estimation.

Abstract: A computer-implemented method is disclosed. The method includes receiving, at an application server from an autonomous vehicle, telematics data, and based upon analysis of the received telematics data, determining, by the application server, that the autonomous vehicle needs to be rescued. The method includes verifying, by the application server, a health status of a communication connection with the autonomous vehicle. The method includes based upon the health status of the communication connection with the autonomous vehicle, performing, by the application server, a plurality of checks to identify one or more problems with a plurality of modules of the autonomous vehicle, and based upon the plurality of checks indicating no failure corresponding to the plurality of modules of the autonomous vehicle, dispatching a service vehicle to a location of the autonomous vehicle to rescue the autonomous vehicle.

Abstract: One or more processors of an autonomous vehicle can determine a surrogate trajectory for navigating the autonomous vehicle towards a target trajectory; generate a curvature target for the autonomous vehicle based on the surrogate trajectory, a lateral error, and a velocity of the autonomous vehicle, the curvature target defined in part by at least three points; and navigate the autonomous vehicle according to the curvature target.

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 plurality of lane width values for the set of image data; labeling the set of image data with the plurality of lane width values, the plurality of lane width values representing a width of a lane in which the at least one autonomous vehicle was positioned; 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 lane width value for a new lane as output.

Abstract: A system includes an in-vehicle controller configured to mount to a vehicle and connect to the drive system of the vehicle, and a remote control configured to send signals to the in-vehicle controller. The remote control includes an input device that is configured to receive commands from a user relating to operation of the vehicle to couple a trailer to the vehicle or decouple the trailer from the vehicle while the user is outside the vehicle and the vehicle and trailer are in a field of view of the user. The signals are based on the user commands and are configured to cause the in-vehicle controller to move the vehicle forward or backward; steer the vehicle; stop movement of the vehicle; and stop movement of the trailer.

Abstract: A system for detecting and reporting cargo lost from a vehicle is disclosed. The system includes a plurality of sensors, and a cargo loss detection system including a processor and a memory device storing instructions that when executed by the processor configure the processor to: (i) receive, from the plurality of sensors, sensor data representing a plurality of cargo loss-related conditions; (ii) determine a cargo loss event has occurred based on a combination of more than one cargo loss-related conditions of the plurality of cargo loss-related conditions; and (iii) transmit a lost cargo detection signal to an external receiver.

Abstract: A blind zone visualization system for an autonomous vehicle is described. The system includes at least one visual sensor mounted on a roof of the autonomous vehicle and at least one mirror mounted on a hood of the autonomous vehicle. The at least one mirror redirects a portion of a field of view (FOV) of the at least one sensor into a volume of space in front of the autonomous vehicle. The system also includes at least one processor programmed to execute computer-readable instructions that cause the at least one processor to receive sensor data output from the at least one visual sensor and representative of the volume of space, detect, from the sensor data, no obstacle is present in the volume of space, and transmit a first signal to a control system of the autonomous vehicle to control the autonomous vehicle to travel forward through the volume of space.