A method for vehicle lane centering includes determining a speed profile over a predetermined period of time to maintain a predetermined speed while a vehicle moves within a lane of a road. The road has a road curve. The predetermined period of time includes a current time and a future time. The method further includes determining a curvature of a vehicle path along the road curve of the road and determining an error of a steering angle command using the speed profile and the curvature of the vehicle path along the road curve of the road. Further, the method further includes determining a variable velocity feedforward control command using the error on the steering angle command and controlling the vehicle using the variable velocity feedforward control command.

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.

Methods and devices for adapting a gain factor of an acceleration controller for a motor vehicle are provided. An acceleration controller specifies an acceleration setpoint for the motor vehicle in a time increment. The acceleration setpoint is specified as a function a speed setpoint of the motor vehicle, an actual speed of the motor vehicle, and the gain factor. The device stores the speed setpoint, the actual speed, and the acceleration setpoint specified as information for at least two time increments, select a first subset of the information, and train a model as a function of the first subset. The model predicts an actual speed in a later time increment from at least one stored actual speed and at least one stored acceleration setpoint, select a second subset of the information, and adapt the gain factor as a function of the second subset, the model and the acceleration controller.

A driving path optimization training method of a vehicle includes: receiving a first data set including a driving path and an associated driving environment information; generating a second data set from the first data by performing data augmentation on the first data; training a driving path planner based on the second data set; and training a driving controller based on a training result of the training of the driving path planner.

A method for applying a Discrete Fourier Transform (DFT) to a sequence of N samples of a sensor signal, with N>2. The resulting DFT spectral coefficients are updated iteratively whenever a new one of the samples or a sub-group of consecutive new samples, comprising a predefined number J of samples, with 1<J<N, is received.

A control device (10) includes: a first calculator (11) that calculates a first equivalent sum value corresponding to a sum of left and right requested torques and a first equivalent difference value corresponding to a difference between the left and right requested torques; a first controller (13) that outputs a first instruction torque by performing a FF control using the first equivalent sum value and the first equivalent difference value; an estimator (14) that estimates, based on the first instruction torque, an estimated sum speed corresponding to a sum of estimated speeds of left and right driving sources (2) and an estimated difference speed corresponding to a difference between the two estimated speeds; a second calculator (15) that calculates a second equivalent sum value corresponding to a sum of two actual speeds of the left and right driving sources (2) and a second equivalent difference value corresponding to a difference between the two actual speeds; a second controller (16) that outputs a second instruction torque by performing a FB control based on a gap between the second equivalent sum value and the estimated sum speed or a gap between the second equivalent difference value and the estimated difference speed; and a third controller (17) that controls outputs of the left and right driving sources (2), using the first and second instruction torques.

An advanced driver-assistance system (ADAS) comprises: a sensor; a behavior planner that performs multi-policy lane change assistance for a vehicle by evaluating multiple scenarios based on an output of the sensor using a cost-based architecture, the cost-based architecture including a Markov decision process (MDP) with a discounted horizon approach applied to pre-chosen open-loop optimistic policies that are time based, wherein the behavior planner uses the MDP for choosing among the pre-chosen open-loop optimistic policies based on respective costs associated with the pre-chosen open-loop optimistic policies, the costs determined by performing a rollout for at least one gap in a fixed time horizon; a motion planner receiving an output of the behavior planner based on the MDP; and a controller receiving an output of the motion planner and determining vehicle dynamics of the vehicle for a next timestep.

A method for vehicle lane centering includes determining a speed profile over a predetermined period of time to maintain a predetermined speed while a vehicle moves within a lane of a road. The road has a road curve. The predetermined period of time includes a current time and a future time. The method further includes determining a curvature of a vehicle path along the road curve of the road and determining an error of a steering angle command using the speed profile and the curvature of the vehicle path along the road curve of the road. Further, the method further includes determining a variable velocity feedforward control command using the error on the steering angle command and controlling the vehicle using the variable velocity feedforward control command.

A vehicle control device includes a processor and a drive motor. The processor may identify whether a host vehicle is in situation where the host vehicle is avoiding a collision, identify a first control amount and a second control amount, and adjust a front wheel control amount, or a rear wheel control amount, such that the front wheel control amount and the rear wheel control amount fall within the specified control amount range based on the front wheel control amount which is identified according to the first control amount and the second control amount and is a control amount of a drive motor that controls the front wheel, the rear wheel control amount which is identified according to the first control amount and the second control amount and is a control amount of a drive motor that controls the rear wheel, and the specified control amount range.

The present disclosure relates to a control unit for a vehicle, the vehicle being a full electric or a hybrid vehicle, the control unit including a communication interface and processing circuitry being coupled to the communication interface; the processing circuitry being adapted to receive, via the communication interface, a message from a remote control device to drive the vehicle a predefined distance, to command the gearbox to engage with the highest available ratio, to command the at least one electric motor in order to move the vehicle, to stop the motor when the vehicle has traversed the predefined distance, and to command to engage the at least one brake.