A device and a method for steering a vehicle are provided. The method includes following steps: obtaining a point cloud data of a vehicle through a lidar, obtaining an RGB image of the vehicle through a camera, and obtaining a current speed of the vehicle through a wheel speed sensor; using the current speed and local path way points associated with the point cloud data to obtain a target angle; using the current speed and a central lane distance error associated with the RGB image to obtain a compensator angle; and using the target angle and the compensator angle to obtain a steering command, and steering the vehicle to drive in a lane according to the steering command.

A vehicle travel control device executes trajectory following control to make the vehicle follow a target trajectory. A delay time represents control delay of the trajectory following control. A delay compensation time is at least a part of the delay time. The trajectory following control includes: displacement estimation processing that estimates a displacement of the vehicle in the delay compensation time; and delay compensation processing that corrects a deviation between the vehicle and the target trajectory based on the estimated displacement to compensate the control delay. The displacement estimation processing is effective in an effective period and ineffective in an ineffective period. When the ineffective period is included in the delay time of the trajectory following control, the displacement estimation processing is executed in a temporary mode by using sensor-detected information in the effective period without using the sensor-detected information in the ineffective period.

A method, device, and computer readable medium for controlling drift of a vehicle. The drift of the vehicle is controlled by acquiring a slip rate level and steering information of the vehicle in a drift mode opening state; determining a target drift parameter according to the slip rate level, the steering information and a current vehicle velocity, the target drift parameter includes a target yaw rate; determining a steering compensation quantity according to a current actual yaw rate and the target yaw rate; determining front axle torque, rear axle torque and rear wheel brake torque according to the steering compensation quantity and the steering information; and controlling the vehicle to drift travelling according to the front axle torque, the rear axle torque and the rear wheel brake torque, and controlling a power-assisted steering motor to perform steering compensation according to the steering compensation quantity and the vehicle velocity.

A lane keep assist system for an automotive vehicle includes an electric power steering assembly that is responsive to an output of the control system, the motor applying a torque to a part of a steering gear to steer the vehicle along a highway. The lane keep assist system assists a driver in keeping the vehicle in a lane of a highway, in which the control circuit comprises A PID Controller which receives at an input a target lane position for the closed-loop control system and provides as an output a control signal for a motor of the electric power steering assembly. The controller is arranged in a closed loop with the motor configured to minimise an error value indicative of the difference between the target lane position and the actual lane position of the vehicle.

A method for controlling gear shifting of a hybrid electric vehicle is applied to a transmission control unit, and includes: transmitting, at an interval of a preset time period and via a vehicle control unit (VCU), a first torque control instruction to a first microcontroller unit (MCU) and a second torque control instruction to a second MCU; transmitting, in response to a difference between a rotational speed of an integrated starter and a rotational speed of the clutch being within a preset speed difference range and via the VCU, a rotational speed control instruction to the first MCU and a gear shift instruction to the second MCU; and transmitting, in response to receiving a TM gear-shifting completion instruction, at an interval of a preset time period and via the VCU, a third torque control instruction to the first MCU and a fourth torque control instruction to the second MCU.

A method for steering system position control includes receiving a target angle for a pinion of a rack of a steering system, receiving an actual angle of the pinion, and determining an angle error value based on a difference between the target angle and the actual angle. The method also includes providing the angle error value to a proportional-integral-derivative (PID) controller, receiving a target velocity of the pinion, and generating a friction compensation value based on the target velocity. The method also includes generating a control command value based on an output of the PID controller and the friction compensation value, and controlling at least one aspect of the steering system based on the control command value.

An automotive vehicle control circuit can include a PID Controller that receives at an input a set point signal for the closed-loop control system and provides as an output a control signal that is fed to the motion control system. The PID controller is arranged in a closed-loop configuration with the motion control system to minimise an error value indicative of the difference between the demanded behaviour of the motion control system as indicated by the demand signal and the actual behaviour of the motion control system. The control circuit can include a neural network which has an input layer of neurons, at least one hidden layer of neurons, and an output layer comprising at least one output neuron, in which the neural network comprises a feedforward neural network that receives at the input layer of input neurons the demand signal, the drive signal output from the controller and the error value. The neural network is configured to determine one or more of the P gain, I gain and D gain terms used by the PID controller, and the neural network receives as a feedforward term at least one additional discrete environmental variable.

The present disclosure provides a cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, a controller, and a storage medium. A distributed state-space equation with state coupling and control input coupling characteristics is established. Meanings and transformation methods of predicted trajectories, assumed trajectories, and optimal trajectories of the states and control inputs are designed, providing a communication basis for information exchange between the agents. In order to coordinate the global performance indexes of a vehicle, a local agent optimization problem considering cost coupling is established, and the influence of the cooperative relationship on the control effect is quantitatively analyzed through adaptive weight coefficients. A method of performing a plurality of iterations within a unit sampling time is adopted, and iteration errors are utilized to enable the controller to achieve a balance between solution accuracy and efficiency.

The present disclosure provides a cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, a controller, and a storage medium. A distributed state-space equation with state coupling and control input coupling characteristics is established. Meanings and transformation methods of predicted trajectories, assumed trajectories, and optimal trajectories of the states and control inputs are designed, providing a communication basis for information exchange between the agents. In order to coordinate the global performance indexes of a vehicle, a local agent optimization problem considering cost coupling is established, and the influence of the cooperative relationship on the control effect is quantitatively analyzed through adaptive weight coefficients. A method of performing a plurality of iterations within a unit sampling time is adopted, and iteration errors are utilized to enable the controller to achieve a balance between solution accuracy and efficiency.

A device and a method for steering a vehicle are provided. The method includes following steps: obtaining a point cloud data of a vehicle through a lidar, obtaining an RGB image of the vehicle through a camera, and obtaining a current speed of the vehicle through a wheel speed sensor; using the current speed and local path way points associated with the point cloud data to obtain a target angle; using the current speed and a central lane distance error associated with the RGB image to obtain a compensator angle; and using the target angle and the compensator angle to obtain a steering command, and steering the vehicle to drive in a lane according to the steering command.