Abstract: A system for monitoring a load distribution of an autonomous vehicle is provided. The system includes a processor in communication with a memory device. The processor is configured to receive weight data from a grid of weight sensors disposed on a cargo bed surface of the autonomous vehicle, generate a weight distribution map of the cargo bed surface based on the received weight data, the weight distribution map including an array of weight values, and control operation of the autonomous vehicle based in part on the generated weight distribution map.

Abstract: Systems and methods for training and executing machine learning models to generate lane index values are disclosed. A method includes identifying a set of image data captured by at least one autonomous vehicle when the at least autonomous vehicle is positioned in a lane of a roadway and respective ground truth localization data; determining a plurality of lane index values for the set of image data based on the ground truth localization data; labeling the set of image data with the plurality of lane index values, the lane index values representing a number of lanes from a leftmost or rightmost lane to the lane in which the at least one autonomous vehicle was positioned; and training, using the labeled set of image data, a plurality of machine learning models that generate a left lane index value and a right lane index value as output.

Abstract: A method for producing a suitable distance between an autonomous vehicle and a second vehicle. The method includes recording data of an environment including the second vehicle driving ahead of the autonomous vehicle along a direction of travel, the data being recorded by a sensor system positioned relative to the autonomous vehicle in the direction of travel and in communication with a computing unit. The method includes detecting a potentially dangerous situation by the sensor system based on a distance between the autonomous vehicle and the second vehicle. The method includes adjusting the autonomous vehicle to maintain the suitable between the autonomous vehicle and the second vehicle.

Abstract: A method of controlling an autonomous vehicle, including: collecting perception data representing a perceived environment of the vehicle using a perception system on board the autonomous vehicle; comparing the perception data collected with digital map data; and modifying operation of the vehicle based on an amount of difference between the perception data and the digital map data.

Abstract: An autonomous vehicle including a network of sensors including a plurality of acoustic sensors and a plurality of visual sensors, at least one processor, and at least one memory storing instructions is disclosed. The instructions, when executed by the at least one processor, cause the at least one processor to: (i) generate spatial beamforming maps locating a sound source based upon acoustic signals received at the plurality of acoustic sensors; (ii) identify a type of an object generating the acoustic signals received at the plurality of acoustic sensors based upon comparison of the acoustic signals with a plurality of acoustic signals and respective objects stored in a dataset; and (iii) generate feature maps for an application in an autonomous vehicle driving by enhancing visualization maps generated based upon visual signals received by the plurality of visual sensors.

Abstract: Systems and methods for training artificial intelligence models based on sequences of image data are disclosed. The techniques described herein include generating, using an artificial intelligence model, a respective classification and a respective bounding box for an object depicted in each image of a sequence of images captured during operation of an autonomous vehicle; tracking the object in the sequence of images based on the respective bounding box of each image of the sequence of images and a tracking identifier corresponding to the object; determining a correction to the respective classification of an image of the sequence of images responsive to tracking the object in the sequence of images; and training the artificial intelligence model based on the correction.

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.