Abstract: A system including a plurality of first wires and a plurality of second wires is disclosed. Each second wire overlaps over one or more first wires at a respective overlapping area of one or more first wires. A dielectric material is disposed in a vertical gap at each respective overlapping area forming a capacitance sensor including a first electrode corresponding to a first wire and a second electrode corresponding to a second wire. The first and second electrodes are connected to a positive bias and a negative bias of at least one voltage source, respectively. The system includes at least one processor configured to periodically measure change in respective electric field of each capacitance sensor of a subset of capacitance sensors of a plurality of capacitance sensors, and store data corresponding to periodically measured change in the respective electric field of each capacitance sensor of the subset of capacitance sensors.

Abstract: An autonomous vehicle including one or more sensors, at least one processor, and at least one memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform operations including receiving sensor data from one or more sensors of an autonomous vehicle, determining, using a first tracking methodology, methodology tracking based on the sensor data, and receiving ground truth tracking. The at least one processor determining an association between the methodology tracking and the ground truth tracking, determining a joint probability distribution of the methodology tracking and the ground truth tracking, determining a methodology tracking distribution based on the joint probability distribution, determining a ground truth tracking distribution based on the joint probability distribution, and determining a Kullback-Leibler Divergence (KLD) score between the methodology tracking distribution and the ground truth tracking distribution.

Abstract: Systems and methods for guiding an autonomous vehicle through known and unknown environments by following a remote vehicle are disclosed. An autonomous vehicle in accordance with the present disclosure comprises a computing system including a processor, a memory device, and at least one sensor configured to receive signals from a remote vehicle. The processor is programmed to receive a message to follow a remote vehicle, the follow instruction message including at least one identifier. The processor is further programmed to detect the remote vehicle, confirm the identity of the remote vehicle using the at least one identifier, and then establish a communication link with the remote vehicle. Once the at least one processor determines that the remote vehicle is aligned with the autonomous vehicle, the at least one processor may transmit one or more instructions which cause the autonomous vehicle to follow the remote vehicle.

Abstract: In one aspect, a vehicle and associated method includes a liquid reservoir, a first sensor mounted in front of the liquid reservoir, a second sensor mounted behind the liquid reservoir, a memory storing instructions, at least one processor configured to access the memory and receive a plurality of inputs from the first and second sensors, the at least one processor being configured to compare a first sensor output from the first sensor to a second sensor output from the second sensor, and based on a determined difference between the first and second sensor outputs, initiate a notification of a detected leak in the liquid reservoir.

Abstract: Disclosed herein are systems, methods, and apparatuses for calibrating sensors of automated vehicles using calibration targets. A bracket holds the calibration targets and attaches to the automated vehicle proximate to a sensor being calibrated. For some sensors, such as GNSS antennas or similar device for receiving location data from a GNSS, a two-target bracket is fixed at the top of the automated vehicle, nearby the GNSS antenna. For IMUs or similar sensors, a three-target bracket is fixed at location of the automated vehicle proximate to the particular IMU, such as a passenger cabin or on the chassis of the automated vehicle. The calibration targets include a retroreflective surface that reflect signals, such as infrared signals, back to a theodolite (or total station). The theodolite or computer includes preprogrammed offset values indicating the relative positions of the sensor being calibrated and each of the calibration targets of the bracket.

Abstract: A computer-implemented method of cargo theft management for an autonomous vehicle includes receiving sensor data from one or more sensors of an autonomous vehicle. The computer-implemented method of cargo theft management for an autonomous vehicle also includes determining a theft condition is present by evaluating the sensor data based on a database of theft conditions. Furthermore, the computer-implemented method also includes sending sensor data related to the theft condition to a mission control computing device, where the mission control computing device is a cloud-based computing device. Additionally, the computer-implemented method also includes determining at least one theft response action based on the theft condition. Also, the computer-implemented method includes executing, via the autonomous vehicle, the at least one theft response action.

Abstract: A system and method for autonomous vehicle sensor cleaning utilizing at least one sensor, a processing system, and memory with executable instructions. The processor receives sensor data, detects obstructions causing operational impact on the vehicle, selects a sensor cleaning solution based on that impact, deploys it through the cleaning system, and checks for nominal sensor operation. The system supports various sensors (optical, electromagnetic, etc.) and obstruction types (dirt, snow, etc.), while determining impact to autonomous operations of the vehicle. The processor may disengage deployment of the cleaning solutions, monitor data post-deployment, and redeploy if necessary.

Abstract: Systems and methods for learning safe driving paths in autonomous driving adversity conditions can include acquiring, by a computer system, vehicle sensor data for a plurality of driving events associated with one or more autonomous driving adversity conditions. The vehicle sensor data can include, for each driving event, corresponding image data depicting surroundings of the vehicle and corresponding inertial measurement data. The computer system can acquire, for each driving event of the plurality of driving events, corresponding vehicle positioning data indicative of a corresponding trajectory followed by the vehicle, and train, using the vehicle sensor data and the vehicle positioning data, a machine learning model to predict navigation trajectories during the autonomous driving adversity conditions.

Abstract: Systems and methods of determining distance bounds for adaptive cruise control. An autonomous vehicle system can identify a second vehicle on the roadway that is traveling in front of the autonomous vehicle; determine a speed of the second vehicle while the second vehicle travels along the roadway; generate a distance bound between the autonomous vehicle and the second vehicle based on the speed of the second vehicle and a speed of the autonomous vehicle; and control the speed of the autonomous vehicle to implement adaptive cruise control according to the distance bound.

Abstract: A method of detecting an object in a path of an vehicle using a LiDAR system includes emitting a LiDAR signal with the LiDAR system; receiving the LiDAR signal with the LiDAR system; determining a glancing angle distance; determining the LiDAR signal is received from beyond the glancing angle distance based on receipt of the LiDAR signal; and classifying the LiDAR signal as a return from an object based at least in part on the LiDAR signal coming from a distance beyond the glancing angle distance.

Abstract: A system for vehicle route planning is provided. The system includes a database configured to electronically store crime data, and a route planning unit configured to generate a route for a vehicle. The system includes a processing device configured to execute instructions stored in a memory to perform operations including receiving as input at the route planning unit a starting location for the vehicle and an ending location for the vehicle. The operations include analyzing the crime data to determine instances of crime occurring between the starting location and the ending location. The system includes generating a planned route for the vehicle from the starting location to the ending location based on the analyzed crime data.

Abstract: Systems and methods of automatic correction of map data for autonomous vehicle navigation are disclosed. An autonomous vehicle system can receive first sensor data from a first sensor of an autonomous vehicle and second sensor data from a second sensor of the autonomous vehicle, the first sensor data and the second sensor data captured during operation of the autonomous vehicle; generate, based on the first sensor data, a first prediction of a dimension of a lane of a road; generate, based on the second sensor data, a second prediction of a position of the autonomous vehicle within the lane of the road; determine a confidence value for the second prediction of the position of the autonomous vehicle within the lane of the road based on the first prediction and the second prediction; and navigate the autonomous vehicle based on the confidence value.

Abstract: An autonomous vehicle comprises a sensor and one or more processors. The one or more processors can be configured to receive a set of object location data from the sensor, the set of object location data indicating an object; generate a measured representation of the object based on the set of object location data; execute a tracking protocol using data of the measured representation of the object as input to generate a virtual representation of the object, the virtual representation of the object comprising a virtual point that is nearest to a defined location of the autonomous vehicle; and translate the virtual point of the virtual representation of the object to a measured point of the measured representation of the object.

Abstract: A system includes at least one processor configured to: (i) determining a first distance between a center point of a wheel of an unloaded vehicle and a chassis spring of the vehicle based on first sensor data of the first set of plurality of image sensors; (ii) determining a second distance between the center point and the chassis spring for the loaded vehicle based on second sensor data of the first set of plurality of image sensors; (iii) based upon a difference between the first distance and the second distance, and characteristics of the chassis spring, determining a load at each wheel for determining a total mass of the vehicle and a first coordinates of a center of gravity of the vehicle along a longitudinal axis and a lateral axis with respect to a front axle of the vehicle.

Abstract: A system for sensor calibration is provided. The system includes a processor in communication with a memory device and a plurality of LiDAR sensors of an autonomous vehicle. The processor is configured to detect, based on data received from a first LiDAR sensor of the plurality of LiDAR sensors, a relative velocity of the autonomous vehicle to a static object while the autonomous vehicle is traveling along a straight trajectory, compute, based on the relative velocity, a vector representing a velocity estimate of the autonomous vehicle, identify the first LiDAR sensor as misaligned when a lateral component of the computed vector is greater than a threshold lateral component, and store, in the memory device, a status of the first LiDAR sensor as misaligned, wherein the autonomous vehicle is controlled based in part on the status of the first LiDAR sensor.

Abstract: The disclosure relates to a protective sleeve for selectively providing a barrier between an undesirable environmental condition and a sensing device. The protective sleeve includes a sleeve body that is movable between a stowed orientation and a deployed orientation. In the deployed orientation the sleeve body produces a barrier between the environmental conditions and the sensing device. The sleeve body is movable between the stowed and deployed orientations by an actuator. The protective sleeve includes a processing system including a sensor for sensing the environmental conditions. The processing system controller and processor programmed to selectively power the actuator when an undesirable environmental condition is sensed by the sensor to move the sleeve body to the deployed orientation, and also selectively power the actuator to cause the actuator to move the sleeve body to the stowed orientation in the absence of an undesirable environmental condition.

Abstract: A system including at least one memory and at least one processor coupled to the at least one memory is disclosed. The at least one processor is configured to: receive, from a sensor on an autonomous vehicle, sensor data; receive, from a camera on the autonomous vehicle, camera data; provide a frame of the camera data overlaid with a respective frame of the sensor data to a user; receive a user annotation indicative of an actor in the frame, where the user annotation encloses a first set of points associated with the actor; generate an a virtual annotation enclosing a second set of points associated with the actor; determine that a point in the second set of points is older than a reference time; project the point to a new position based on a kinematic model; generate a temporally aggregated actor to determine a quality of the user input.

Abstract: A system for signaling intent of an autonomous vehicles. The system is comprised of a memory storing executable instructions representing a signaling display module on a trailer of an autonomous vehicle. The system also comprises a processor coupled to the memory and configured to execute the signaling display module. The processor is configured to receive data from an autonomy computing system descriptive of a condition near the autonomous vehicle, process the data to generate a message relating to the condition indicating a remedial action of the autonomous vehicle in response to the condition, and transmit the message to a signaling display on the trailer prior to executing the remedial action.

Abstract: Trailer detection is provided. A vehicle can determine a distance to a portion of the trailer from the time-of-flight sensor data. The vehicle can determine a doppler shift indicative of a speed of a wheel of a wheel assembly from the data. The vehicle can determine, based on the distance and the doppler shift, a wheelbase of the trailer.