A method for setting an anticipator module with which a control device controls the trajectory of a motor vehicle is equipped includes detecting whether the anticipator module is unsuitable during a turn by taking account of a lateral deviation with respect to an ideal trajectory and/or a contribution of a feedback module of the control device, determining primary parameters, calculating a secondary parameter by an optimization-based calculation method taking account of the determined primary parameters, and updating a bicycle model of the vehicle by taking account of the calculated secondary parameter.

A driver command interpreter system for a vehicle includes one or more controllers that execute instructions to receive a plurality of dynamic variables, vehicle configuration information, and driving environment conditions, and determine a target vehicle state during transient driving conditions based on the plurality of dynamic variables from the one or more sensors, the vehicle configuration information, and the driving environment conditions. The one or more controllers build a transient vehicle dynamic model based on the target vehicle state during transient driving conditions, the plurality of dynamic variables, the vehicle configuration information, and the driving environment conditions, and solve for desired zeros corresponding to the target vehicle state during transient conditions.

Embodiments of the present invention provide a method of controlling a vehicle, comprising predicting a first parameter of a vehicle state at each of a plurality of points in time in dependence on a first parameter of a current vehicle state and a first model associated with the vehicle, predicting a second parameter of the vehicle state at each of the plurality of points in time in dependence on a second parameter of the current vehicle state, the predicted first parameter of the vehicle state and a second model associated with the vehicle, and determining one or more control inputs for the vehicle at each of the points in time in dependence on the predicted first and second parameters of the vehicle state at each of the plurality of points in time and desired first and second parameters of the vehicle state at each of the plurality of points in time.

A vehicle can include throttle, braking, and steering systems. The vehicle can further include a computing system that obtains, from one or more sensors, data representing one or more of a velocity or an acceleration of the vehicle. The computing system can further determine an estimated weight of the vehicle based on the one or more of the velocity or the acceleration of the vehicle, and autonomously operate the throttle, braking, and steering systems of the vehicle based on the estimated weight of the vehicle.

Methods of and systems for adaptive cruise control (ACC) of a vehicle include a controller for the vehicle that is configured to execute a computer-implemented model predictive control and a safe spacing policy that reduces collision risk between the vehicle and both a leading vehicle and a following vehicle. The methods include sensing a speed of a leader vehicle in front of the ego vehicle and a distance of the leader vehicle from the ego vehicle, sensing a speed of a follower vehicle behind the ego vehicle and a distance of the follower vehicle from the ego vehicle, and controlling the speed of the ego vehicle to avoid collision with the leader vehicle while reducing risk of the ego vehicle being hit by the follower vehicle.

An apparatus for controlling a vehicle includes a vehicle additional yaw moment calculator that calculates, based on a yaw rate of a vehicle, a vehicle additional yaw moment to be applied to the vehicle independently of a steering system, a slipping condition determiner that makes a determination as to a slipping condition of the vehicle, and an adjustment gain calculator that calculates an adjustment gain to adjust the vehicle additional yaw moment so as to reduce the vehicle additional yaw moment additional yaw moment when the vehicle is determined to be in the slipping condition, and increases the adjustment gain in accordance with a degree of a slip of the vehicle when the vehicle is determined to recover from the slipping condition.

A vehicle can include throttle, braking, and steering systems. The vehicle can further include a computing system that obtains, from one or more sensors, data representing one or more of a velocity or an acceleration of the vehicle. The computing system can further determine an estimated weight of the vehicle based on the one or more of the velocity or the acceleration of the vehicle, and autonomously operate the throttle, braking, and steering systems of the vehicle based on the estimated weight of the vehicle.

A computing system determines an estimated weight of a vehicle by measuring kinematic data of the vehicle, including at least one of a velocity or an acceleration of the vehicle. The computing system processes the data to determine an estimated weight of the vehicle. Based on the estimated weight of the vehicle, the computing system can autonomously operate the throttle, braking, and steering systems of the vehicle.

A method is described for determining interface conditions between a tire and the ground in a motor vehicle, particularly to determine an onset of an aquaplaning phenomena. The method includes: determining a reference longitudinal acceleration of the vehicle, measuring an actual longitudinal acceleration of the vehicle, calculating a difference between the reference longitudinal acceleration and the actual longitudinal acceleration, determining an additional drag at the interface between tire and ground on the basis of the difference, and a lift at the interface between tire and ground on the basis of the additional drag, and determining a threshold force at which a lifting of the tire from the ground occurs, comparing the lift with the threshold force and determining a degree of proximity of the interface conditions between tire and ground to an aquaplaning condition.

Systems and methods are provided for uncertainty based contingency model predictive control of a vehicle in uncertain road conditions. Applications may be found for collision imminent steering, on-road autonomous vehicles and real time decision-making influenced by an unknown environment. An uncertainty road coefficient of friction may be estimated using an Unscented Kalman filter, and the controller may be updated based upon the estimated uncertainties to provide for collision avoidance in unknown conditions.