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 system and method for estimating road conditions ahead of a vehicle, including: a LIDAR sensor operable for generating a LIDAR point cloud; a processor executing a road condition estimation algorithm stored in a memory, the road condition estimation algorithm performing the steps including: detecting a ground plane or drivable surface in the LIDAR point cloud; superimposing an M×N matrix on at least a portion of the LIDAR point cloud; for each patch of the LIDAR point cloud defined by the M×N matrix, statistically evaluating a relative position, a feature elevation, and a scaled reflectance index; and, from the statistically evaluated relative position, feature elevation, and scaled reflectance index, determining a slipperiness probability for each patch of the LIDAR point cloud; and a vehicle control system operable for, based on the determined slipperiness probability for each patch of the LIDAR point cloud, affecting an operation of the vehicle.

A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold. A velocity output register registers the estimated lateral velocity of the vehicle when the lateral acceleration error is smaller than the predefined threshold, and a sideslip calculator calculates a sideslip angle of the vehicle in real time from the registered lateral velocity of the vehicle and a vehicle longitudinal velocity.

A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold. A velocity output register registers the estimated lateral velocity of the vehicle when the lateral acceleration error is smaller than the predefined threshold, and a sideslip calculator calculates a sideslip angle of the vehicle in real time from the registered lateral velocity of the vehicle and a vehicle longitudinal velocity.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: predict, at a trained machine learning classifier, a target slip value based on a predicted slip slope and a predicted road texture, wherein the predicted slip slope and the predicted road texture are determined using sensor data representing tire forces and modify at least one vehicle action based on the target slip value when a confidence level value corresponding to the target slip value is greater than or equal to a confidence level threshold.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: predict, at a trained machine learning classifier, a target slip value based on a predicted slip slope and a predicted road texture, wherein the predicted slip slope and the predicted road texture are determined using sensor data representing tire forces and modify at least one vehicle action based on the target slip value when a confidence level value corresponding to the target slip value is greater than or equal to a confidence level threshold.

A multi-layer path-planning system and method calculates trajectories for autonomous vehicles using a global planner, a fast local planner, and an optimizing local planner. The calculated trajectories are used to guide the autonomous vehicle along a bounded path between a starting point and a destination.

A multi-layer path-planning system and method calculates trajectories for autonomous vehicles using a global planner, a fast local planner, and an optimizing local planner. The calculated trajectories are used to guide the autonomous vehicle along a bounded path between a starting point and a destination.

A method for determining a friction coefficient for a contact between a tire of a vehicle and a roadway. The method includes processing sensor signals in order to generate processed sensor signals. The sensor signals represent state data that are read in at least by at least one detection device and that are correlatable with the friction coefficient. The processed sensor signals represent at least one preliminary friction coefficient. The method also includes ascertaining the friction coefficient using the processed sensor signals and a regression model.

A vehicle control device configured to control switching of drive mode of a vehicle including an internal combustion engine and a motor includes a processor configured to switch, in a case where a road surface of a perimeter of a geofencing zone is a road surface on which there is a high probability that the vehicle slips, in a movement route from an outside of the geofencing zone to an inside of the geofencing zone, the drive mode of the vehicle to drive by the motor in a state in which there is a low probability that the vehicle slips, outside the geofencing zone.