A method (100) of determining driving state variables of a motor vehicle (105) includes scanning an input vector (u) of signals, which influence the driving state of the motor vehicle (105); scanning a first output vector (y) of variables, which describe the driving state of the motor vehicle (105); determining a second output vector () of variables that describe the driving state of the motor vehicle (105) based on the input vector (u), a weighting vector (r) and a state vector ({circumflex over (x)}); and adapting the weighting vector based on the difference between the two output vectors (y, ). In doing so, the observer includes a Kalman filter.

A system for controlling a vehicle a sensor to sense measurements indicative of a state of the vehicle and a memory to store a motion model of the vehicle, a measurement model of the vehicle, and a mean and a variance of a probabilistic distribution of a state of calibration of the sensor. The motion model of the vehicle defines the motion of the vehicle from a previous state to a current state subject to disturbance caused by an uncertainty of the state of calibration of the sensor in the motion of the vehicle. The measurement model relates the measurements of the sensor to the state of the vehicle using the state of calibration of the sensor. The system includes a processor to update the probabilistic distribution of the state of calibration based on a function of the sampled states of calibration weighted with weights determined based on a difference between the state of calibration sampled on a feasible space defined by the probabilistic distribution and the corresponding state of calibration estimated based on the measurements using the motion and the measurements models. The system includes a controller to control the vehicle using the measurements of the sensor adapted using the updated probabilistic distribution of the state of calibration of the sensor.

A system for controlling a motion of a vehicle from an initial state to a target state includes a path planner to determine a discontinuous curvature path connecting the initial state with the target state by a sequential composition of driving patterns. The discontinuous curvature path is collision-free within a tolerance envelope centered on the discontinuous curvature path. The system further includes a path transformer to locate and replace at least one treatable primitive in the discontinuous curvature path with a corresponding continuous curvature segment to form a modified path remaining within the tolerance envelope. Each treatable primitive is a predetermined pattern of elementary paths. The system further includes a controller to control the motion of the vehicle according to the modified path.

Path planning method for computing optimal parking maneuvers for road vehicles including the steps of computing a set of value functions of a cost function of parking maneuvers reaching the target set of states as unique viscosity solution of a Hamilton Jacobi Bellman equation, supplying the set of value functions, together with a starting state of the vehicle, as input to the dynamic programming calculation procedure calculating at least the set of vehicle controls. The set of equations modeling the evolution of the state of said road vehicle is a switched system of equations between a first sub-system if the vehicle is in forward motion and a second sub-system if the vehicle is in reverse motion. The cost function takes into account the arrival time a number of direction changes of the road vehicle between forward motion and reverse motion.

A system vectors torque between two wheels of a rear axle of an electric motor vehicle that are disposed on either side of the vehicle and are each driven by an electric motor in order to distribute a torque between the two wheels. The system includes a first torque setpoint generator, a second slip correction torque generator, a detector for detecting oversteer or understeer of the vehicle during the acceleration phase when turning, a third skid correction torque generator, a corrected-torque set point generator, and a controller for controlling the first electric motor based on the first corrected-torque set point and for controlling the second electric motor based on the second corrected-torque setpoint.

A drive system for a work vehicle includes a chassis, a first front wheel and a second front wheel operably coupled with a front axle assembly, and a first rear wheel and a second rear wheel operably coupled with a rear axle assembly. At least one wheel sensor can be associated with the first front wheel, the second front wheel, or both. A transfer case can be operably coupled with a front differential input shaft and a rear differential input shaft. A computing system can be operably coupled with the at least one wheel sensor and the transfer case. The computing system can be configured to receive an input related to a commanded vehicle trajectory, determine a correlation of a front axle reference point to a rear axle reference point, and determine a differential shaft ratio based at least partially on the correlation.

A travel control apparatus (140) includes a acquisition unit (141), which acquires information on a target track through which a vehicle (M) will pass in the future, a parameter determiner (142), which determines a parameter by which a track model defined by one or more parameters coincides with a track acquired by the acquisition unit, and a steering controller (143), which feedforward-controls steering of the vehicle on the basis of at least the track model defined by the parameter determined by the parameter determiner, wherein the parameter determiner determines the parameter on the basis of a direction of a change in the degree of separation between the track model and the target track with respect to a change in the parameter.

A motion of a vehicle is controlled according to a sequential compositions of the elementary paths following a transformation of one of a first pattern, a second pattern, and a third pattern. The first pattern defines a forward motion of the vehicle from a first state to a second state while turning left followed by a backward motion of the vehicle from the second state to a third state while turning right, wherein the orientation of the first state is opposite to the orientation of the second state, and wherein the orientation of the first state is equal to the orientation of the second state. The second pattern defines the motion of the vehicle from a fourth state to a fifth state while moving left, wherein the orientation of the fifth state is leftward perpendicular to the orientation of the fourth state. The third pattern defines a forward motion of the vehicle from a sixth state to a seventh state while turning first left and then right followed by a backward motion of the vehicle from the seventh state to an eighth state while turning first right and then left followed by a forward motion of the vehicle from the eight state to a ninth state while turning first left and then right, wherein the orientation of the sixth state equals the orientation of the seventh state and equals the orientation of the eighth state and equals the orientation of the ninth state. The functions representing the patterns are stored in a memory and are used, in response to receiving an initial state and a target state of the vehicle, to determine parameters of the minimum-curvature path. The motion of the vehicle is controlled according to the parameters of the minimum-curvature path.

A method for determining non-directly measurable driving status variables of a vehicle reads in by a sensor device and transmits to a computing device the following: wheel speed of each vehicle wheel, steering angle of the vehicle, yaw angle rate, longitudinal road inclination of the vehicle, transverse road inclination of the vehicle.

Driving status variables are calculated by the computing device with a computational model, so that further driving variables that are difficult to measure or not directly measurable can be determined on the basis of the calculated driving status variables. The calculated and determined variables are transmitted to an actuator device to control and/or regulate the vehicle. The computational model contains a vehicle model, a tire model, and a wheel suspension model and are solved together in the computing device according to the following differential equation system:

[00001]$\left(M_F+\underset{i=1}{\overset{n?3}{.Math.}}{M}_{\mathrm{Ri}}\right)\stackrel{.fwdarw.}{\stackrel{.}{q}}+\left({\stackrel{.fwdarw.}{k}}_F+\underset{i=1}{\overset{n?3}{.Math.}}{\stackrel{.fwdarw.}{k}}_{\mathrm{Ri}}\right)+\left({\stackrel{.fwdarw.}{b}}_g\underset{i=1}{\overset{n?3}{.Math.}}{\stackrel{.fwdarw.}{b}}_i\right)=\stackrel{.fwdarw.}{0}$

The present disclosure provides a cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, a controller, and a storage medium. A distributed state-space equation with state coupling and control input coupling characteristics is established. Meanings and transformation methods of predicted trajectories, assumed trajectories, and optimal trajectories of the states and control inputs are designed, providing a communication basis for information exchange between the agents. In order to coordinate the global performance indexes of a vehicle, a local agent optimization problem considering cost coupling is established, and the influence of the cooperative relationship on the control effect is quantitatively analyzed through adaptive weight coefficients. A method of performing a plurality of iterations within a unit sampling time is adopted, and iteration errors are utilized to enable the controller to achieve a balance between solution accuracy and efficiency.