A controller and a method for controlling motion of a vehicle is provided. The method comprises acquiring motion information including a current state of the vehicle and a desired state of the vehicle, determining a combination of a steering angle of the wheels and motor forces for moving the vehicle from the current state into the desired state by using a first model of the motion of the vehicle and a second model of the motion of the chassis of the vehicle, determining a cost function of the motion of the vehicle, optimizing the cost function of the motion of the vehicle to compute a command signal for controlling the steering wheel and the plurality of electric motors, and controlling the steering angle of the wheels and the motor forces based on the command signal.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statutes for performing safe driving operation. Example embodiments relate to an autonomous vehicle having a trailer coupled to a rear thereof. An example method includes continuously predicting a trailer trajectory that is distinct from a planned trajectory of the autonomous vehicle. The method further includes determining that the predicted trailer trajectory is within a minimum avoidance distance away from a stationary vehicle located on a roadway on which the autonomous vehicle is located. The method further includes modifying the planned trajectory of the autonomous vehicle such that the predicted trailer trajectory satisfies the minimum avoidance distance. The method further includes causing the autonomous vehicle to navigate along the modified trajectory based on transmitting instructions to one or more subsystems of the autonomous vehicle.

A method and system for operating a vehicle's trailer reverse assist system is disclosed, including receiving vehicle sensor system data from one or more sensors supported by the vehicle. Based upon the received vehicle sensor data, a trailer pitch angle and a trailer roll angle of a coupled trailer are estimated relative to the vehicle. Surface unevenness of a surface traversed by the vehicle and the trailer is estimated, based upon the estimated trailer pitch angle and the estimated trailer roll angle. One or more parameters of the trailer reverse assist system or of the tow vehicle are adjusted based upon the estimated road surface variance. The trailer reverse assist system of the tow vehicle is subsequently operated using the adjusted one or more parameters.

A number of illustrative variations may include a system and method of controlling vehicle slowing while implementing brake-to-steer functionality that may include providing a feed-forward gain on vehicle propulsion torque to achieve or maintain target longitudinal acceleration and replicate the behavior of a vehicle not using brake-to-steer. The system may manipulate propulsion of the vehicle to manage longitudinal acceleration disturbance and speed disturbance during brake-to-steer.

A number of illustrative variations may include a system including brake-to-steer algorithms may achieve lateral control of a vehicle without longitudinal compensation but may also force a vehicle to slow down too rapidly before appropriate lateral movement can be achieved and may deliver an unnatural driving experience for vehicle occupants. A more natural feeling deceleration may be achieved by optimally selecting appropriate transmission shifts to allow for optimal engine speed or electric motor speed and torque based on current vehicle speed thereby reducing undesirably longitudinal disturbance.

The present disclosure relates to an eco-friendly vehicle in which valet mode can be controlled with detailed control levels, and a method for controlling the same mode. A method for controlling valet mode of a vehicle according to an embodiment of the present disclosure comprises determining whether an entry condition for the valet mode to be satisfied, determining, if the condition being satisfied, one level among a plurality of levels for the valet mode based on at least one of a vehicle state and a driving environment and performing a vehicle control according to the determined level, wherein the plurality of levels are different from one another in whether or not a restriction to be made to at least part of a powertrain and a function or a degree of the restriction.

This application discloses an intelligent vehicle control method. An intelligent vehicle control system obtains a driving mode, a driving style model, and a target speed of an intelligent vehicle at a current moment, then determines a speed control instruction based on the driving mode and the driving style model, and sends the speed control instruction to an execution system of the intelligent vehicle. This provides an intelligent vehicle control method with high comfort and good experience.

A parallel computing method for man-machine coordinated steering control of a smart vehicle based on risk assessment is provided, comprising the following steps: building a lateral kinetic equation model of a vehicle; building a target function by targeting at minimizing an offset distance of a vehicle driving track from a lane center line and making a change in a front wheel steering angle and a longitudinal acceleration as small as possible in a driving process; building a parallel computing architecture of a prediction model and the target function, and employing a triggering parallel computing method; solving and computing a gradient with a manner of back propagation and using a gradient descent method to obtain an optimal control amount of the front wheel steering angle and an optimal control amount of the longitudinal acceleration; and computing a driving weight, obtaining a desired front wheel steering angle and completing real time control.

A method for perception fields driving related operations, the method may include (i) obtaining object information regarding one or more objects located within an environment of a vehicle; (ii) determining, using one or more neural network (NNs), one or more virtual forces that are applied on the vehicle, wherein the one or more virtual forces represent one or more impacts of the one or more objects on a behavior of the vehicle; wherein the one or more virtual forces belong to a virtual physical model; and (iii) performing one or more driving related operations of the vehicle based on the one or more virtual forces.

Technologies and techniques for determining a trajectory of an assisted-operated motor vehicle. At least one object is detected in an environment of the motor vehicle and at least one uncertainty with respect to the object is determined. A future environment with the object is predicted via an electronic computing device, as a function of the detected environment and the detected object, wherein a risk value for a planned trajectory is determined on the basis of a collision probability. A most probable impact constellation and accident severity for the most probable impact constellation is determined, wherein the collision probability and the accident severity is weighted in a risk value, and wherein the trajectory is determined as a function of the determined risk value.