A backup control unit for controlling motion of a heavy-duty vehicle during a minimum risk maneuver, where the backup control unit is arranged to receive data indicative of a planned sequence of vehicle control commands from a main vehicle control unit. The backup control unit comprises a first vehicle model configured to map the planned sequence of vehicle control commands into a desired vehicle behavior and is arranged to obtain a measured vehicle behavior from one or more vehicle sensors. Also, the back-up control unit is arranged to determine an adjusted sequence of vehicle control commands based on the planned sequence of vehicle control commands and on a deviation between the desired vehicle behavior and the measured vehicle behavior, and to transmit the adjusted sequence of vehicle control commands to a motion support device, MSD, control unit of the vehicle.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A method for determining a tread depth of a tread of a tire during operation of a vehicle having the tire, a control device for a vehicle for determining a tread depth of a tread of a tire of the vehicle, and a system for a vehicle having such a control device and at least one electronic wheel unit, are provided. Provision is made to determine the tread depth based on a determined instantaneous dynamic wheel radius of a wheel, having the tire, of the vehicle and a determined instantaneous dynamic inside radius of the tire. In addition, at least one further first operating parameter of the tire, selected from the group including an instantaneous roadway gradient, an instantaneous vehicle drive mode and an instantaneous tire material expansion, is determined and taken into consideration.

A method, an apparatus, a storage medium, and an electronic device for testing dynamic parameter of vehicle are provided. The method for testing dynamic parameter of vehicle provided by the present disclosure includes: first obtaining a control parameter for an autonomous vehicle; then controlling the vehicle to travel automatically under a given environment according to the control parameter, detecting and recording traveling data of the vehicle; and at last determining a dynamic parameter of the vehicle according to the traveling data. According to the method for testing dynamic parameter provided by the present disclosure, the characteristic of automatic driving of an autonomous vehicle is utilized to achieve an automatic measurement of the dynamic parameter, thereby reducing cost for calibrating the vehicle and significantly improving safety during the test. Additionally, human error caused by manually driving during the test can be avoided effectively.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

The present disclosure relates to a method and system for integrated path planning and path tracking control of an autonomous vehicle. The method includes: obtaining five input control variables and eleven system state variables of an autonomous vehicle at current time; constructing a vehicle path planning-tracking integrated state model according to the obtained variables at the current time; enveloping external contours of two autonomous vehicles using elliptical envelope curves to determine elliptical vehicle envelope curves of the two autonomous vehicles, respectively; determining time to collision (TTC) between the vehicles according to elliptical vehicle envelope curves and vehicle driving states; establishing an objective function of a model prediction controller (MPC) according to the model; and solving the objective function based on the TTC, and determining input control variables to the MPC at the next time. Autonomous vehicle collision avoidance can be achieved according to the present disclosure.

A control device for a vehicle drive unit is configured to control, based on an operating state of a vehicle, a vehicle drive unit having one or more power sources. The control device includes a processor and a storage device. The storage device is configured to store a vehicle front-rear acceleration prediction model being a machine learning model that receives as an input a command torque and outputs predicted acceleration. The processor is configured to: execute a predicted acceleration calculation process using the vehicle front-rear acceleration prediction model; and execute a command torque calculation process to calculate the command torque that minimizes an evaluation function. The evaluation function minimizes a deviation of the predicted acceleration with respect to a target vehicle front-rear acceleration according to a target torque based on the operating state while reducing a deviation of the command torque with respect to the target torque.

A method for estimating a friction between a road surface and a tire of a steered wheel of a vehicle. The steered wheel being fit with dynamic steering. The vehicle includes a steering wheel and a set of sensors comprising wheel end sensors and steering wheel sensors configured to measure signals corresponding to a set of parameters., The steering wheel parameters comprising at least a steering wheel torque and a steering wheel angle. The method comprising the following steps implemented by the electronic control unit collect the signals, corresponding to the set of parameters, measured by the sensors during a period of time; process, by the signal processing module, the signals collected to provide processed signal data provide the processed signal data as input to the wheel end friction estimation model, the wheel end friction estimation model being configured to output a friction estimation of the friction between the road surface and the tire of the wheel.

A control method, relating to the technical field of automobile intelligent driving includes: determining a target parking spot, and automatically generating target track points, an estimated braking distance, and an estimated coasting distance; calculating the longitudinal distance between the current position and the target end point according to the target track points and the current control deviation; collecting real-time vehicle driving information, and calculating current vehicle speed, slope, and vehicle braking response time information;

updating the longitudinal distance at a fixed frequency according to the longitudinal distance to the target end point and the real-time vehicle speed; on the basis of control state decision logic, performing real-time estimation of the distance to the target parking point to determine the vehicle control state. A system for fixed-point parking in autonomous driving includes a vehicle information collection module, a position estimation module, and a control state decision module.

A device for controlling the movement of a motor vehicle, including a longitudinal controller and a lateral controller which are capable of generating, from first information relating to the road layout and second information relating to the dynamic behaviour of the vehicle, control commands intended for actuators for controlling the longitudinal and lateral movement of the vehicle. The device includes a prediction model which is supplied with the first and second information and is capable of determining future states of the vehicle for future positions of the vehicle over a plurality of iterations defining a future road portion. The model is connected to a module for determining whether driving limit values are violated, which module is capable of determining, for each future state, whether one of the state variables defining the future state reaches or exceeds a driving limit value, and of deducing a future risk situation.