This document describes target curvature estimation considering vehicle dynamics constraints. Some vehicle dynamics models use several kinematic states to predict the paths of targets identified by an object-tracking system. Rather than rely on predetermined industry measurements as the kinematic inputs to a model, an example system instead estimates some of these states using near real-time data output from a tracking filter. This can include estimating position and velocity states, as well as curvature and centripetal acceleration states. The example system is particularly suited at accurately predicting the curvature and centripetal acceleration states, which greatly improves the accuracy of the object-tracking system, and further improves safety in avoiding a target collision.

A system for training an operator of a vehicle includes a processor and a memory in communication with the processor, which includes a safety module and a training module. The safety module has instructions that, when executed by the processor, cause the processor to determine when the vehicle is operating within a safe area based on at least one of: a location of the vehicle and a location of one or more objects with respect to the vehicle. The training module has instructions that, when executed by the processor, cause the processor to apply at least one brake of the vehicle when the vehicle is operating within the safe area to cause the vehicle to engage in an oversteer event, and collect operator response information when the vehicle engages in the oversteer event.

An estimation includes a dead reckoning unit, a map matching unit, and a parameter correction unit. The dead reckoning unit is configured to estimate a state quantity by dead reckoning based on (i) a dynamics model including a parameter that changes a behavior of the vehicle and (ii) internal information acquired from an inside of the vehicle. The map matching unit is configured to observe the state quantity by map matching based on (i) map information indicative of a traveling environment of the vehicle and (ii) external information acquired from an outside of the vehicle. The parameter correction unit is configured to correct the parameter, which is to be fed back for the dead reckoning, based on an offset amount that is a difference between the state quantity observed by the map matching and the state quantity estimated by the dead reckoning.

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 device and a method for improving a turning motion of a vehicle may improve turning stability by cooperative control of an electric motor and the electronic controlled suspension (ECS) and improve behavior stability by optimizing a pitch/roll behavior by allowing realization of a target yaw moment required to improve turning characteristic of the vehicle to be reinforced by not only a yaw moment directly generated by a braking torque or a driving torque of the electric motor, but also a yaw moment indirectly generated by a load movement caused by controlling a damping force of the electronic controlled suspension (ECS).

A method for implementing a closed loop of an advanced driving aid device for the lateral control of a motor vehicle includes synthesizing a controller of the closed loop by solving an optimization problem based on a bicycle model of the vehicle. A family of at least two bicycle models of the vehicle is established, these models having, with respect to one another, at least one dispersion chosen from among a dispersion of mass of the vehicle, a dispersion of drift rigidity on a drivetrain of the vehicle, a dispersion of the center of gravity of the vehicle, and a dispersion of the position of the matrix of inertia of the vehicle, the optimization problem being solved on the basis of all models of the family.

An object of the invention is to realize an M+ control which is suitable to a driving scene without depending on pedal operation information of a driver. A vehicle motion control device according to the invention sets an absolute value of deceleration generated in the vehicle in a period in which the lateral motion of the vehicle is predicted to be changed from a state where the vehicle takes the lateral motion to a state where the vehicle does not take the lateral motion to be smaller than that generated in a period in which the lateral motion of the vehicle is predicted to be changed from a state the vehicle takes one of right and left lateral motions to a state where the vehicle takes the other lateral motion.

A vehicle control apparatus according to the present invention outputs a signal regarding a target braking/driving force for guiding a vehicle in a target traveling direction to a braking/driving controller. The signal regarding the target braking/driving force is acquired based on information regarding a running route of the vehicle and a physical amount regarding a motion state of the vehicle. The vehicle control apparatus outputs a signal regarding a steering correction torque for correcting a steering torque according to a behavior of the vehicle to a steering force controller. The signal regarding the steering correction torque is acquired based on a vehicle-body slip angle of the vehicle and the target braking/driving force.

A method to control, while driving along a curve, a road vehicle with a variable stiffness and with rear steering wheels. The method comprises the steps of: determining an actual attitude angle of the road vehicle; establishing a desired attitude angle; determining an actual yaw rate of the road vehicle; establishing a desired yaw rate; and changing, in a simultaneous and coordinated manner, the steering angle of the rear wheels and the distribution of the stiffness of the connection of the four wheels to the frame depending on a difference between the actual attitude angle and the desired attitude angle and depending on a difference between the actual yaw rate and the desired yaw rate.

A method and system of controlling a vehicle includes providing a plurality of dynamic state inputs to a controller in the vehicle that is adapted to execute a plurality of control loops and further includes determining an operating mode of the vehicle. Based on the operating mode of the vehicle, a location of an optimum perceived yaw center of the vehicle is determined corresponding to a selected estimation technique using the dynamic state inputs and wherein the estimation technique is selected based upon the determined operating mode of the vehicle. The information related to the location of the optimum perceived yaw center may be used as input for controlling the vehicle in a dynamic state.