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.

Apparatus for estimating a grip factor of at least one wheel comprising: a control unit configured to process a current slip angle of said wheel; a storage unit, within which, in a consultation table, a plurality of grip curves correlating a plurality of values of a steering parameter comprising the rack force with a plurality of values of the slip angle are recorded; along a same curve, the value of the grip factor remains unchanged; wherein the control unit is configured to cyclically estimate at least one raw value of the grip factor of said at least one wheel based on the position of a current condition within the consultation table, as a function of the current slip angle and of the current rack force.

A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data; setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.

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).

Techniques for improving the performance of an autonomous vehicle (AV) are described herein. A system can determine a plan for the AV in a driving scenario that optimizes an initial cost function of a control algorithm of the AV. The system can obtain data describing an observed human driving path in the driving scenario. Additionally, the system can determine for each cost dimension in the plurality of cost dimensions, a quantity that compares the estimated cost to the observed cost of the observed human driving path. Moreover, the system can determine a function of a sum of the quantities determined for each cost dimension in the plurality of cost dimensions. Subsequently, the system can use an optimization algorithm to adjust one or more weights of the plurality of weights applied to the plurality of cost dimensions to optimize the function of the sum of the quantities.

Methods and systems are provided for using the weights of cost functions to improve linear-program-based vehicle driveline architectures and systems. In some embodiments, the methods and systems may include establishing values for driveline controls of a linear program based on driveline requests of the linear program. The values of the driveline controls, which may be used to adjust driveline actuators, may be established based on values of a plurality of weights of a cost function of the linear program, the weights respectively corresponding with the plurality of driveline requests.

An apparatus for controlling a distance from a front vehicle includes a receiver to receive information on a host vehicle and information on the front vehicle, an acceleration generator to generate one of first acceleration for the host vehicle or second acceleration for the host vehicle, based on the received information on the host vehicle and the received information on the front vehicle and an output device to output the generated first acceleration or the generated second acceleration.

A sensor information fusion method of an embodiment includes obtaining N sensor tracks from each of a plurality of sensors with respect to a target located around a vehicle, calculating association costs of the N sensor tracks with respect to M reference tracks, and storing the association costs in a matrix form, and calculating an arrangement of reference tracks and sensor tracks that minimize the association costs with respect to the matrix, and outputting a sensing information result with respect to the target according to the arrangement of the reference tracks and the sensor tracks calculated by the plurality of sensors.

An apparatus of controlling an engine including an electric supercharger includes: an engine to combust fuel to generate power; a drive motor to assist the power of the engine and selectively operate as a generator to generate electrical energy; a battery configured to supply electrical energy to the drive motor and to be charged by the electrical energy generated from the drive motor; a plurality of electric superchargers respectively installed in a plurality of intake lines through which an ambient air flows to be supplied to a combustion chamber of the engine; and a controller that based on a determined driving tendency, adjusts a target speed of the electric superchargers of the plurality of electric superchargers, determine a driving mode of the electric superchargers, limits a maximum output of the engine, and variably adjusts a SOC electricity-generating region where the engine charges the battery.

This application is directed to augmenting training data used for vehicle driving modelling. A computer system obtains a first image of a road and identifies a drivable area of the road within the first image. The computer system obtains an image of an object and generates a second image from the first image by overlaying the image of the object over the drivable area. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for facilitating driving of a vehicle (e.g., at least partial autonomously). In some embodiments, the computer system applies machine learning to train a model using the corpus of training images and distributes the model to one or more vehicles. In use, the model processes road images captured by the one or more vehicles to facilitate vehicle driving.