When a control program is updated by a device that can select and execute one of two control programs, an amount of data to be transferred is reduced to shorten time required for update of a program. A first rewritable storage unit that stores two programs, a CPU that selects and executes one of the two programs, and a second rewritable storage unit that stores common data accompanying the two programs are included.

A method and an apparatus for automated driving, a device, and a computer-readable storage medium are provided. The method for automated driving includes: obtaining data associated with a parameter and an external environment of a vehicle, the data being collected according to a collection policy for a target scenario for the vehicle; generating an automated driving model for the target scenario based on the obtained data; and updating the collection policy by testing the automated driving model.

Systems and methods for dynamic predictive control of autonomous vehicles are disclosed. In one aspect, an in-vehicle control system for a semi-truck includes one or more control mechanisms configured to control movement of the semi-truck and a processor. The system further includes computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to receive a desired trajectory and a vehicle status of the semi-truck, determine a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, determine at least one quadratic program (QP) problem based on the dynamic model, generate at least one control command for controlling the semi-truck by solving the at least one QP problem, and provide the at least one control command to the one or more control mechanisms.

Systems and methods for controlling a vehicle. The system includes one or more sensors positioned on the vehicle and configured to sense an environment surrounding the vehicle, a collision mitigation subsystem configured to control a braking system of the vehicle, and an autonomous driving subsystem communicatively coupled to the one or more sensors and the collision mitigation subsystem. The autonomous driving subsystem is configured to receive sensor information from the one or more sensors and generate, based on the sensor information, a model of the environment surrounding the vehicle. The autonomous driving subsystem is configured to determine, based on the model of the environment surrounding the vehicle, a plurality of possible trajectories for the vehicle and to select, from the plurality of possible trajectories, a travel path for the vehicle. The autonomous driving subsystem is configured to transmit the travel path to the collision mitigation subsystem.

This disclosure relates to method and system for validating an Autonomous Vehicle (AV) stack. The method may include receiving an Operational Design Domain (ODD) and real-world data for evaluating at least one of an Advanced Driver Assistance System (ADAS) and the AV. The ODD is based on at least one feature of at least one of the ADAS and the AV. For each of a plurality of iterations, the method may further include generating a driving scenario based on the ODD of the AV and the real-world data through a Quality of Ride Experience (QoRE)-aware cognitive engine, plugging and running at least one of the ADAS and the AV algorithm based on the driving scenario, and determining a set of performance metrics corresponding to the at least one feature of at least one of the ADAS and the AV in the driving scenario based on the simulating.

Vehicle control is provided, including: obtaining vehicle information of a target vehicle and environmental information of a reference environment in which the target vehicle is located; obtaining a target matrix based on the vehicle information and the environmental information; splitting the target matrix to obtain a plurality of sub-matrices; and obtaining target driving control information of the target vehicle based on matrix elements in the sub-matrices and driving control information of a surrounding vehicle of the target vehicle.

A simulation apparatus that generates a gradient pattern of a running road for a vehicle and a vehicle speed pattern for simulation. The apparatus includes a gradient pattern generation unit configured to generate a gradient pattern of a running road by sequentially deriving a gradient for each section, and a vehicle speed pattern generation unit configured to generate a vehicle speed pattern by, in a model including a lead vehicle that runs in accordance with a first vehicle speed pattern, a rearmost vehicle that runs in accordance with a first preceding vehicle following algorithm, and one or more intermediate vehicles that increase or reduce by one for each first period at an equal probability between the lead vehicle and the rearmost vehicle and that runs in accordance with the first preceding vehicle following algorithm, deriving a vehicle speed of the rearmost vehicle.

A method includes generating, while a vehicle is operating in an autonomous-driving mode, a planned trajectory associated with a computing system of the vehicle based on first sensor data capturing an environment of the vehicle. The method further includes, while the vehicle is operating according to the planned trajectory, receiving a disengagement instruction associated that causes the vehicle to disengage from operating in the autonomous-driving mode and switch to operating in a disengagement mode. Subsequent to the vehicle operating in the disengagement mode, the method further includes capturing second sensor data and generating a simulation of the environment. The simulation is based on sensor data associated with the environment and the planned trajectory. Additionally, subsequent to the vehicle operating in the disengagement mode, the method concludes with evaluating a performance of an autonomy system based on the simulation, and providing feedback based on the evaluation.

Methods of determining which kinematic model an autonomous vehicle (AV) should use to predict motion of a detected moving actor are disclosed. One or more sensors of the AV sensors will detect a moving actor. The AV will assign one or more probable classes to the actor, and it will process the information to determine a kinematic state of the actor. The system will query a library of kinematic models to return one or more kinematic models that are associated with each of the probable classes. The system will apply each of the returned kinematic models to predict trajectories of the actor. The system will then evaluate each of the forecasted trajectories of the actor against the kinematic state of the actor to select one of the returned kinematic models to predict a path for the actor. The system will then use the predicted path to plan motion of the AV.

A dynamic velocity planning method for an autonomous vehicle is performed to plan a best velocity curve of the autonomous vehicle. An information storing step is performed to store an obstacle information, a road information and a vehicle information. An acceleration limit calculating step is performed to calculate the vehicle information according to a calculating procedure to generate an acceleration limit value range. An acceleration combination generating step is performed to generate a plurality of acceleration combinations according to the obstacle information, the road information, and the acceleration limit value range. An acceleration filtering step is performed to filter the acceleration combinations according to a jerk threshold and a jerk switching frequency threshold to obtain a selected acceleration combination. An acceleration smoothing step is performed to execute a driving behavior procedure to adjust the selected acceleration combination to generate the best velocity curve.