Abstract: A system and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to a normal driving behavior safe zone; receiving a proposed vehicle control command; comparing the proposed vehicle control command with the normal driving behavior safe zone; and issuing a warning alert if the proposed vehicle control command is outside of the normal driving behavior safe zone. Another embodiment includes modifying the proposed vehicle control command to produce a modified and validated vehicle control command if the proposed vehicle control command is outside of the normal driving behavior safe zone.

Abstract: The application discloses a method, system and related device of implementing vehicle automatically weighing, so as to achieve the automatically weighing of the unmanned vehicle. The method includes: controlling, by a vehicle controller, a vehicle to drive automatically and stop at a weighing position; weighing, by a weighbridge sensor, the vehicle when sensing the vehicle stopping at the weighing position, and sending weighing end information to the vehicle controller; and controlling, by the vehicle controller, the vehicle to start and leave the weighing position when receiving the weighing end information.

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 operating a rear-facing perception system for vehicles are described. An exemplary rear-facing perception system contains two corner units and a center unit, with each of the two corner units and the center unit including a camera module and a dual-band transceiver. A method for operating the rear-facing perception system includes pairing with a control unit by communicating, using the dual-band transceiver, over at least a first frequency band, transmitting a first trigger signal to the two corner units over a second frequency band non-overlapping with the first frequency band, and switching to an active mode. In an example, the first trigger signal causes the two corner units to switch to the active mode, which includes orienting the camera modules on the center unit and the two corner units to provide an unobstructed view of an area around a rear of the vehicle.

Abstract: Disclosed are devices, systems and methods for capturing an image. In one aspect an electronic camera apparatus includes an image sensor with a plurality of pixel regions. The apparatus further includes an exposure controller. The exposure controller determines, for each of the plurality of pixel regions, a corresponding exposure duration and a corresponding exposure start time. Each pixel region begins to integrate incident light starting at the corresponding exposure start time and continues to integrate light for the corresponding exposure duration. In some example embodiments, at least two of the corresponding exposure durations or at least two of the corresponding exposure start times are different in the image.

Abstract: Disclosed are devices, systems and methods for processing an image. In one aspect a method includes receiving an image from a sensor array including an x-y array of pixels, each pixel in the x-y array of pixels having a value selected from one of three primary colors, based on a corresponding x-y value in a mask pattern. The method may further include generating a preprocessed image by performing preprocessing on the image. The method may further include performing perception on the preprocessed image to determine one or more outlines of physical objects.

Abstract: Various embodiments of the present disclosure provide a system and method for iterative lane marking localization that may be utilized by autonomous or semi-autonomous vehicles traveling within the lane. In an embodiment, the system comprises a locating device adapted to determine the vehicle's geographic location; a database; a region map; a response map; a plurality of cameras; and a computer connected to the locating device, database, and cameras, wherein the computer is adapted to receive the region map, wherein the region map corresponds to a specified geographic location; generate the response map by receiving information from the camera, the information relating to the environment in which the vehicle is located; identifying lane markers observed by the camera; and plotting identified lane markers on the response map; compare the response map to the region map; and iteratively generate a predicted vehicle location based on the comparison of the response map and the region map.

Abstract: A system and method for semantic segmentation using hybrid dilated convolution (HDC) are disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image; performing a convolution operation on the feature map and producing multiple convolution layers; grouping the multiple convolution layers into a plurality of groups; applying different dilation rates for different convolution layers in a single group of the plurality of groups; and applying a same dilation rate setting across all groups of the plurality of groups.

Abstract: Various embodiments of the present disclosure provide a system and method for lane marking localization that may be utilized by autonomous or semi-autonomous vehicles traveling within the lane. In an embodiment, the system comprises a locating device adapted to determine the vehicle's geographic location; a database; a region map; a response map; a camera; and a computer connected to the locating device, database, and camera, wherein the computer is adapted to: receive the region map, wherein the region map corresponds to a specified geographic location; generate the response map by receiving information from the camera, the information relating to the environment in which the vehicle is located; identifying lane markers observed by the camera; and plotting identified lane markers on the response map; compare the response map to the region map; and generate a predicted vehicle location based on the comparison of the response map and the region map.

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.