Abstract: Disclosed are devices, systems, and methods for a LiDAR mirror assembly mounted on a vehicle, such as an autonomous or semi-autonomous vehicle. For example, a LiDAR mirror assembly may include a base plate mounted on a hood of a vehicle, where the base plate is coupled to one end of a support arm. The opposite end of the support arm is attached to a housing. The housing includes a top housing enclosure coupled to a bottom housing platform, where a sensor and a mirror is coupled to the housing, and where the sensor is at least partially exposed through an opening in the top housing enclosure. The opening for the sensor is situated near an end of the housing furthest away from the base plate.

Abstract: Techniques are described for managing redundant steering system for a vehicle. A method includes sending a first control command that instructs a first motor coupled to a steering wheel in a steering system to steer a vehicle, receiving, after sending the first control command, a speed of the vehicle, a yaw rate of the vehicle, and a steering position of the steering wheel, determining, based at least on the speed and the yaw rate, an expected range of steering angles that describes values within which the first motor is expected to steer the vehicle based on the first control command, and upon determining that the steering position of a steering wheel is outside the expected range of steering angles, sending a second control command that instructs a second motor coupled to the steering wheel in the steering system to steer the vehicle.

Abstract: Devices, systems, and methods for a vehicular safety system in autonomous vehicles are described. An example method for safely controlling a vehicle includes selecting, based on a first control command from a first vehicle control unit, an operating mode of the vehicle, and transmitting, based on the selecting, the operating mode to an autonomous driving system, wherein the first control command is generated based on input from a first plurality of sensors, and wherein the operating mode corresponds to one of (a) a default operating mode, (b) a minimal risk condition mode of a first type that configures the vehicle to pull over to a nearest pre-designated safety location, (c) a minimal risk condition mode of a second type that configures the vehicle to immediately stop in a current lane, or (d) a minimal risk condition mode of a third type that configures the vehicle to come to a gentle stop.

Abstract: A system and method for three-dimensional (3D) object detection is disclosed. A particular embodiment can be configured to: receive image data from a camera associated with a vehicle, the image data representing an image frame; use a machine learning module to determine at least one pixel coordinate of a two-dimensional (2D) bounding box around an object in the image frame; use the machine learning module to determine at least one vertex of a three-dimensional (3D) bounding box around the object; obtain camera calibration information associated with the camera; and determine 3D attributes of the object using the 3D bounding box and the camera calibration information.

Abstract: Systems and methods of managing error data associated with a vehicle to provide a more user-friendly vehicle error data system. A method of managing error data associated with a vehicle incorporates storing error data in a memory; categorizing the error data via topics or time frame via a processor coupled to the memory; analyzing the error data to result in at least one topic thread or a time thread; selecting desired error data in response to selection of the desired error data from the error data by a user; converting the desired error data into readable information via the processor; and displaying the readable information via a user interface providing a human-machine interaction.

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: The system and method make 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 commence 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. Finally, the computer model for the vehicle is modified.

Abstract: Techniques are described for determining weight distribution of a vehicle. A method of performing autonomous driving operation includes determining a vehicle weight distribution that values for each axle of the vehicle that describe weight or pressure applied on a respective axle. The values of the vehicle weight distribution are determined by removing at least one value that is outside a range of pre-determined values from a set of sensor values. The method further includes determining a driving-related operation of the vehicle weight distribution. For example, the driving-related operation may include determining a braking amount for each axle and/or determining a maximum steering angle to operate the vehicle. The method further includes controlling one or more subsystems in the vehicle via an instruction related to the driving-related operation. For example, transmitting the instruction to the one or more subsystems causes the vehicle to perform the driving-related operation.

Abstract: Methods, systems, and devices related to a method of controlling an autonomous vehicle, in particular, an autonomous diesel-engine truck are disclosed. In one example aspect, the method includes determining an available engine brake torque generation mechanism for reducing a current speed of the autonomous vehicle to a lower speed and selecting a brake mode corresponding to the engine brake torque availability. In case a rate of speed reduction is equal to or smaller than a threshold, the brake mode includes only an engine brake in which engine exhaust valve opening is adjusted for reducing the current speed. The threshold determined in part based on the available engine brake torque, gear position of the transmission, and the online estimated vehicle longitudinal dynamic model. In case the rate of speed reduction is greater than the threshold, the brake mode incudes a combination of the engine brake and the foundation brake.

Abstract: A lead autonomous vehicle (AV) includes a sensor configured to observe a field of view in front of the lead AV. Following AVs are on the same road behind the lead AV. A processor of the lead AV is configured to detect a road structural change on the particular road. The processor updates driving instructions of the lead AV to navigate through the structural change using driving instructions related to the structural change. The processor sends a first message to the operation server indicating that the structural change is detected. The operation server updates the first portion of the map data, reflecting the structural change. The operation server sends the updated map data to the one or more following AVs.

Abstract: A system and method for using human driving patterns to manage speed control for autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to desired human driving behaviors; training a human driving model module using a reinforcement learning process and the desired human driving behaviors; receiving a proposed vehicle speed control command; determining if the proposed vehicle speed control command conforms to the desired human driving behaviors by use of the human driving model module; and validating or modifying the proposed vehicle speed control command based on the determination.

Abstract: A system and method for vehicle wheel detection is disclosed. A particular embodiment can be configured to: receive training image data from a training image data collection system; obtain ground truth data corresponding to the training image data; perform a training phase to train one or more classifiers for processing images of the training image data to detect vehicle wheel objects in the images of the training image data; receive operational image data from an image data collection system associated with an autonomous vehicle; and perform an operational phase including applying the trained one or more classifiers to extract vehicle wheel objects from the operational image data and produce vehicle wheel object data.

Abstract: An example method for performing multi-sensor collaborative calibration on a vehicle includes obtaining, from at least two sensors located on a vehicle, sensor data items of an area that comprises a plurality of calibration objects; determining, from the sensor data items, attributes of the plurality of calibration objects; determining, for the at least two sensors, an initial matrix that describes a first set of extrinsic parameters between the at least two sensors based at least on the attributes of the plurality of calibration objects; determining an updated matrix that describes a second set of extrinsic parameters between the at least two sensors based at least on the initial matrix and a location of at least one calibration object; and performing autonomous operation of the vehicle using the second set of extrinsic parameters and additional sensor data received from the at least two sensors.

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: A system and method for proximate vehicle intention prediction for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; extract features from the perception data to detect a proximate vehicle in the vicinity of the host vehicle; generate a trajectory of the detected proximate vehicle based on the perception data; use a trained intention prediction model to generate a predicted intention of the detected proximate vehicle based on the perception data and the trajectory of the detected proximate vehicle; use the predicted intention of the detected proximate vehicle to generate a predicted trajectory of the detected proximate vehicle; and output the predicted intention and predicted trajectory for the detected proximate vehicle to another subsystem.

Abstract: The disclosed technology enables automated parking of an autonomous vehicle. An example method of performing automated parking for a vehicle comprises obtaining, from a plurality of global positioning system (GPS) devices located on or in an autonomous vehicle, a first set of location information that describes locations of multiple points on the autonomous vehicle, where the first set of location information are associated with a first position of the autonomous vehicle, determining, based on the first set of location information and a location of the parking area, a trajectory information that describes a trajectory for the autonomous vehicle to be driven from the first position of the autonomous vehicle to a parking area, and causing the autonomous vehicle to be driven along the trajectory to the parking area by causing operation of one or more devices located in the autonomous vehicle based on at least the trajectory information.

Abstract: A system includes an autonomous vehicle (AV) comprising a sensor, a control subsystem, and an operation server. The control subsystem receives sensor data comprising location coordinates of the AV from the sensor. The operation server detects an unexpected event from the sensor data, comprising at least one of an accident, an inspection, and a report request. The operation server receives a message from a user comprising a request to access particular information regarding the AV and location data. The operation server associates the AV with the user if the location coordinates of the AV match location data of the user. The operation server establishes a communication path between the user and a remote operator for further communications.

Abstract: A system and method for fisheye image processing can be configured to: receive fisheye image data from at least one fisheye lens camera associated with an autonomous vehicle, the fisheye image data representing at least one fisheye image frame; partition the fisheye image frame into a plurality of image portions representing portions of the fisheye image frame; warp each of the plurality of image portions to map an arc of a camera projected view into a line corresponding to a mapped target view, the mapped target view being generally orthogonal to a line between a camera center and a center of the arc of the camera projected view; combine the plurality of warped image portions to form a combined resulting fisheye image data set representing recovered or distortion-reduced fisheye image data corresponding to the fisheye image frame; generate auto-calibration data representing a correspondence between pixels in the at least one fisheye image frame and corresponding pixels in the combined resulting fisheye image

Abstract: An autonomous vehicle can classify and prioritize agent of interest (AOI) objects located around the autonomous vehicle to manage computational resources. An example method performed by an autonomous vehicle includes determining, based on a location of the autonomous vehicle and based on a map, an area in which the autonomous vehicle is operated, determining, based on sensor data received from sensors located on or in the autonomous vehicle, attributes of objects located around the autonomous vehicle, where the attributes include information that describes a status of the objects located around the autonomous vehicle, selecting, based at least on the area, a classification policy that includes a plurality of rules that are associated with a plurality of classifications to classify the objects, and for each of the objects located around the autonomous vehicle: monitoring an object according to a classification of the object based on the classification policy.

Abstract: A vehicle position and velocity estimation system based on camera and LIDAR data is disclosed. An embodiment includes: receiving input object data from a subsystem of a vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device, the distance measuring device comprising one or more LIDAR sensors; determining a first position of a proximate object near the vehicle from the image data; determining a second position of the proximate object from the distance data; correlating the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data; determining a three-dimensional (3D) position of the proximate object using the correlated first and second positions; and using the 3D position of the proximate object to navigate the vehicle.