A processor unit (3) is configured to execute an autonomous driving function of the motor vehicle (1) during a first instance such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function. The processor unit (3) is further configured to store a driver intervention, the driver intervention being performed by a driver of the motor vehicle (1) during the first instance while the motor vehicle (1) travels autonomously based on the execution of the autonomous driving function. Additionally, the processor unit (3) is configured to execute the autonomous driving function during a second instance, subsequent to the first instance, based at least in part on the stored driver intervention such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function according to the stored driver intervention.

A processor unit is configured for determining target torque values (21), which lie within a prediction horizon (20), and target speed values (19), which lie within the prediction horizon (20), by executing an MPC algorithm, which includes a longitudinal dynamics model of a drive train of the motor vehicle. An autonomous driving function of the motor vehicle is carried out in a torque specification operating mode or in a speed specification operating mode as a function of the level of the target torque values (21). In the torque specification operating mode, a prime mover of the drive train is controlled by an open-loop system based on the target torque values (21). In the speed specification operating mode, a speed governor of the drive train is controlled by an open-loop system based on the target speed values (19).

A driving support apparatus includes a feedback control system. The feedback control system calculates each operation amount of a brake actuator and a drive actuator so as to match an actual value of a control amount indicating a motion state of the vehicle to a target value. The target value of the control amount is set so as to stop the vehicle to a target stop position. The driving support apparatus sets, when remaining distance from a current position of the vehicle to the target stop position is first distance, a feedback gain of the feedback control system to large value, as compared with the feedback gain set when the remaining distance is second distance which is greater than the first distance.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: estimate a slip condition corresponding to at least one vehicle wheel; and generate, via an explicit Nonlinear Model Predictive Control (NMPC) module, control data for operating the at least one vehicle wheel based on the estimated slip condition. The explicit Nonlinear Model Predictive Control (NMPC) module includes a feedforward control module that is configured to generate adjustment data based on the estimated slip condition, wherein the adjustment data modifies the control data.

A vehicle may have actuators, including a drive device with a drive motor that can act on a drive wheel, a brake device with a brake that can act on a drive wheel, and/or a steering device with a steering sensor by way of which the steering angle of a wheel is adjustable, a vehicle movement controller, and a setpoint value input means, a setpoint value processing means for detecting setpoint value settings of the setpoint value input means, to calculate a yaw acceleration setpoint value and translational acceleration setpoint values from the setpoint value settings. The setpoint value processing means may be configured to transfer the calculated yaw acceleration setpoint value and translational acceleration setpoint values to the vehicle movement controller, which is configured to actuate one or more of the actuators such that the yaw acceleration setpoint value and the translational acceleration setpoint values are reached.

A vehicle cruise control method and apparatus related to the technical field of vehicle manufacturing are provided. The method resolves a problem of relatively large energy consumption during vehicle cruise control.

The disclosure relates to a vehicle control method. The vehicle control method includes: receiving a motion parameter of a vehicle; detecting, based on the motion parameter, whether there is a longitudinal obstacle in front of the vehicle; and when it is detected that there is a longitudinal obstacle in front of the vehicle, generating compensation torque based on the detected longitudinal obstacle, to perform compensation for control torque of the vehicle to generate required torque of the vehicle. The disclosure further relates to a vehicle control device, a computer-readable storage medium, and a vehicle.

A system for operating a hybrid electric vehicle includes a hybrid starter generator generating a counter-electromotive force while rotating with an engine when conditions are satisfied, including where starting of the engine is secured, a main relay is turned off, and an engine clutch is opened according to a request for reverse traveling, in a state where a component of a high-voltage system is failed, a motor reversely rotating using the counter-electromotive force of the hybrid starter generator, an electronic load component operated using the counter-electromotive force of the hybrid starter generator, and a controller determining an engine speed required by load output values according to operations of the motor and the electronic load component, and restricting the operation of the motor or the electronic load component according to a determined engine speed.

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.

Provided is a processor-implemented method and a processor in a vehicle for estimating the value of a quantity for which a physical sensor is not available for measurement. The method includes: receiving a plurality of measured signals representing values of measurable variables; computing, in real-time, time derivatives of the measured signals; and applying a trained feedforward neural network, in real-time, to estimate values for a plurality of unmeasurable variables, the unmeasurable variables being variables that are unmeasurable in real-time, the feedforward neural network having been trained using test data containing time derivatives of values for the measurable variables and values for the unmeasurable variables; wherein the vehicle uses the estimated values for the unmeasurable variables for vehicle operation.