A method of controlling a vehicle having wheels provided with tires resting on a surface, the method using a model of the physical behavior of each tire as a function of a sideslip angle (β.sub.ij) for each tire relative to the surface. The model is obtained by implementing an adaptive algorithm that selectively applies an affABREGEine model (Z1), a DUGOFF model (Z2), or a constant model (Z3).

The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

The present teaching relates to method, system, medium, and implementation of human-like vehicle control for an autonomous vehicle. Information related to a target motion to be achieved by the autonomous vehicle is received, wherein the information includes a current vehicle state of the autonomous vehicle. A first vehicle control signal is generated with respect to the target motion and the given vehicle state in accordance with a vehicle kinematic model. A second vehicle control signal is generated in accordance with a human-like vehicle control model, with respect to the target motion, the given vehicle state, and the first vehicle control signal, wherein the second vehicle control signal modifies the first vehicle control signal to achieve human-like vehicle control behavior.

Embodiments of this application disclose a vehicle control method and device, where the method includes: calculating a longitudinal force interference compensation torque and a lateral force interference compensation torque of a vehicle when a flat tire occurs in the vehicle; calculating a feedback control torque of the vehicle; determining an additional yaw moment based on the longitudinal force interference compensation torque, the feedback control torque, and the lateral force interference compensation torque; and controlling, based on the additional yaw moment, a wheel in which the flat tire occurs.

An advanced driver assistance system for a heavy-duty vehicle. The ADAS includes a road geometry determining device arranged to determine a geometry of a road section in a forward direction ahead of the vehicle, and a vehicle motion management module configured to predict a swept area by the vehicle when driving in the forward direction, based on a geometric model of the vehicle and on a current vehicle control command, wherein the swept area by the vehicle comprises an area traversed by an overhang of the vehicle. The ADAS further includes a display device configured to illustrate the geometry of the road section and the predicted swept area by the vehicle in dependence of the current vehicle control command.

Example embodiments relate to a method for cut-in identification and classification. An example embodiment includes a obtaining operational data about one or more vehicles; based on the operational data, identifying the presence of one or more cut-ins within the operational data; extracting, from the operational data, cut-in data that depicts one or more of the cut-ins identified within the operational data; and, based on the extracted cut-in data, training a model for controlling an autonomous vehicle. Identifying the presence of a given cut-in includes: determining that at least one vertex of a bounding box surrounding a vehicle was located more than a threshold distance within a lane being navigated by a given vehicle; and determining that the ability of the given vehicle to maintain its course and speed was impeded by the presence of the particular additional vehicle within the lane.

A method of managing operation of a machine is described herein. The machine includes drive components that supply a propulsive force exerted by the machine on a traveled surface. The machine includes a programmed controller that controls power output by a motor to the drive components of the machine, in accordance with a slippage model, to actively manage excessive slippage at a physical interface between the machine and the traveled surface. The programmed controller determines a track force indicative of the propulsive force exerted by the machine on the traveled surface. The programmed controller further determines a modeled slippage based, at least in part, upon the track force and the slippage model. The machine conditionally causes a reduction of the power output by the motor based upon a comparison, by the programmed controller, between the modeled slippage and a slippage limit.

A method, apparatus and computer program product are described so as to provide more additional information regarding vehicular behavior. In the context of a method, information is received regarding a location of the vehicle at a plurality of instances in time. The plurality of instances in time define a time period. The method also includes determining an environmental condition at the location during the time period and comparing the behavior of the vehicle to that of other vehicles at the location that are also subjected to the environmental condition. Additionally, the method may determine a score for the vehicle in relation to a risk factor based upon the behavior of the vehicle and the comparison to other vehicles at the location that are also subjected to the envir onmental condition.

A vehicle driving control method with optimal efficiency includes a first step of state variable modeling of a longitudinal dynamics equation of a vehicle based on a velocity-related state variable and a wheel drive input variable, a second step of calculating wheel power using the state variable and the input variable, a third step of calculating battery power using the wheel power calculation, a fourth step of approximating the battery power, and a fifth step of outputting a wheel drive control target by calculating a minimum solution by using the approximated battery power as an objective function and applying at least one constraint to the objective function.

A vehicle dynamics control system receives a feedback state signal including values of a roll rate and a roll angle of the motion of the vehicle and updates parameters of a model of roll dynamics of the vehicle by fitting the received values into the roll dynamics model. The roll dynamics model explains the evolution of the roll rate and the roll angle based on the parameters including a center of gravity (CoG) parameter modeling a location of a CoG of the vehicle, and a spring constant and a damping coefficient modeling suspension dynamics of the vehicle. The system determines a control command for controlling at least one actuator of the vehicle using a motion model including the updated CoG parameter and submits the control command to the vehicle controller to control the motion of the vehicle.