Abstract: A method of LiDAR-based vehicle tracking for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps including taking point clusters that belong to vehicles in a single LiDAR frame as input, tracking each vehicle as a probabilistic distribution, assigning each tracked vehicle to a corresponding one of the point clusters, and recalculating the probabilistic distribution of each tracked vehicle.

Abstract: A system and method for instance-level lane detection for autonomous vehicle control includes: receiving training image data from a training image data collection system; performing a training phase to train a plurality of tasks associated with features of the training image data, the training phase including extracting roadway lane marking features from the training image data, causing the plurality of tasks to generate task-specific predictions based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including extracting roadway lane marking features from the image data, causing the plurality of trained tasks to generate instance-level lane detection results.

Abstract: The present disclosure provides a method and an apparatus for determining an attitude angle of a camera, capable of improving the accuracy of the attitude angle of the camera, and in turn the accuracy of the attitude of the camera that is obtained based on the attitude angle of the camera. The present disclosure can also improve the accuracy of object distance measurement and vehicle positioning based on the attitude angle of the camera. In the method for determining an attitude angle of a camera, the camera is fixed to one and the same rigid object in a vehicle along with an Inertial Measurement Unit (IMU).

Abstract: The present disclosure provides a cooling system. The cooling system includes: a first set of fans mounted on an inward-facing side of an air inlet on an outer shell of a case; a second set of fans mounted on an inward-facing side of an air outlet on the outer shell of the case, for generating, in cooperation with the first set of fans, a high-pressure airflow from the air inlet to the air outlet; a first heat sink connected to heat generating component in the case, for absorbing heat from the heat generating component and transferring the absorbed heat to a second heat sink; and the second heat sink mounted on an inward-facing side of the second set of fans and cooled by the high-pressure airflow.

Abstract: A system and method for multitask processing for autonomous vehicle computation and control includes: receiving training image data from a training image data collection system; performing a training phase to train a plurality of tasks associated with features of the training image data, the training phase including extracting common features from the training image data, causing the plurality of tasks to generate task-specific predictions based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including extracting common features from the image data, causing the plurality of trained tasks to concurrently generate task-specific predictions based on the image data.

Abstract: A system and method for automated lane change control for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; use the perception data to determine a state of the host vehicle and a state of proximate vehicles detected near to the host vehicle; determine a first target position within a safety zone between proximate vehicles detected in a roadway lane adjacent to a lane in which the host vehicle is positioned; determine a second target position in the lane in which the host vehicle is positioned; and generate a lane change trajectory to direct the host vehicle toward the first target position in the adjacent lane after directing the host vehicle toward the second target position in the lane in which the host vehicle is positioned.

Abstract: A system and method for automated lane change control for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; use the perception data to determine a state of the host vehicle and a state of proximate vehicles detected near to the host vehicle; determine a first target position within a safety zone between proximate vehicles detected in a roadway lane adjacent to a lane in which the host vehicle is positioned; determine a second target position in the lane in which the host vehicle is positioned; and generate a lane change trajectory to direct the host vehicle toward the first target position in the adjacent lane after directing the host vehicle toward the second target position in the lane in which the host vehicle is positioned.

Abstract: The present disclosure provides a method, an apparatus and a system for multi-module scheduling, capable of solving at least one of the problems associated with the multi-module scheduling technique in the related art, i.e., inconsistency in data inputted to a computing module, and a significant delay or low throughput in data transmission between computing modules.

Abstract: The angle of a trailer with respect to a tow vehicle is an important parameter to the stability of the vehicle and trailer. A tow vehicle pulling a trailer in a straight line is generally more stable than when the vehicle is turning. While turning, the angle between the tow vehicle and the trailer is not a straight line but is another angle depending on how sharply the tow vehicle is turning. To safely operate a vehicle towing a trailer, for a given steering input and speed, there is a maximum angle between the tow vehicle and trailer whereby exceeding the angle causes instability and may cause the trailer or tow vehicle to roll over or jackknife. Accordingly, the angle between the trailer and tow vehicle must be determined to ensure the vehicle and trailer will continue to be in control.

Abstract: A system for generating a ground truth dataset for motion planning is disclosed. The system includes: a collecting module configured to collect LiDAR scans; a detecting module configured to detect two pose estimates that are closest to LiDAR scan accusation time; a determining module configured to determine LiDAR's poses based on an interpolation; and a transforming module configured to transform LiDAR raw scans into undistorted LiDAR scans.

Abstract: A Method and System for Modeling Autonomous Vehicle Behavior. The system and method makes it feasible to develop an autonomous vehicle control system for complex vehicles, such as for cargo trucks and other large payload vehicles. The method and system commences by first obtaining 3-dimensional data for one or more sections of roadway. Once the 3-dimensional roadway data is obtained, that data is used to run computer simulations of a computer model of a specific vehicle being controlled by a generic vehicle control algorithm or system. The generic vehicle control algorithm is optimized by running the simulations utilizing the 3-dimensional roadway data until an acceptable performance result is achieved. Once an acceptable simulation is executed using the generic vehicle control algorithm, the control algorithm/system is used to run one or more real-world driving tests on the roadway for which the 3-dimensional data was obtained.

Abstract: A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

Abstract: Devices, systems and methods for controlling an exterior and dashboard lights of an autonomous vehicle are described. One example of a method for controlling one or more exterior lights includes receiving, from an autonomous driving system (ADS) of the vehicle, an input to control one or more exterior lights that are part of a lighting system of the vehicle, and transmitting, based on the input, a message to a controller area network (CAN) bus of the lighting system, the message being further based on a driver command upon a determination that a driver-initiated message is received. In an example, the lighting system of the vehicle further comprises a plurality of dashboard lights.

Abstract: A method of LiDAR-based vehicle tracking for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: projecting points in a processed point cloud to a unit sphere, for each of the projected points, determining that a distance between one and each of its neighboring points is smaller than a threshold, and forming a cluster of the one and its neighboring points.

Abstract: A method of LiDAR-based vehicle tracking for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps including: calculating the probability of a cluster pair that belongs to a same vehicle using tracking information from a previous frame and spatial information from current frame, determining that the clusters of the cluster pair are merged into one by taking both spatial information and tracking information into consideration, and merging the clusters into a single, complete cluster.

Abstract: An autonomous vehicle simulation system for analyzing motion planners is disclosed. A particular embodiment includes: receiving map data corresponding to a real world driving environment; obtaining perception data and configuration instructions and data including pre-defined parameters and executables defining a specific driving behavior for each of a plurality of simulated dynamic vehicles; generating simulated perception data for each of the plurality of simulated dynamic vehicles based on the map data, the perception data, and the configuration instructions and data; receiving vehicle control messages from an autonomous vehicle control system; and simulating the operation and behavior of a real world autonomous vehicle based on the vehicle control messages received from the autonomous vehicle control system.

Abstract: A system for generating a ground truth dataset for motion planning is disclosed. The system includes sensors for data acquisition for motion planning of a vehicle, the sensors including a LiDAR; a calculating module configured to calculate LiDAR poses in response to data from the sensors and generate undistorted LiDAR scans; an extracting module configured to extract static points from a target undistorted LiDAR scan and generate a LiDAR static-scene point cloud; and a generating module configured to generate sparse image point correspondences for each pair of images, using the LiDAR static-scene point cloud.

Abstract: A system and method for image localization based on semantic segmentation are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on an autonomous vehicle; performing semantic segmentation or other object detection on the received image data to identify and label objects in the image data and produce semantic label image data; identifying extraneous objects in the semantic label image data; removing the extraneous objects from the semantic label image data; comparing the semantic label image data to a baseline semantic label map; and determining a vehicle location of the autonomous vehicle based on information in a matching baseline semantic label map.

Abstract: A system and method for generating simulated vehicles with configured behaviors for analyzing autonomous vehicle motion planners are disclosed. A particular embodiment includes: receiving perception data from a plurality of perception data sensors; obtaining configuration instructions and data including pre-defined parameters and executables defining a specific driving behavior for each of a plurality of simulated dynamic vehicles; generating a target position and target speed for each of the plurality of simulated dynamic vehicles, the generated target positions and target speeds being based on the perception data and the configuration instructions and data; and generating a plurality of trajectories and acceleration profiles to transition each of the plurality of simulated dynamic vehicles from a current position and speed to the corresponding target position and target speed.