An exemplary method includes obtaining vehicle data comprising environmental perception data indicating a risk assessment regarding one or more perceived elements of an environment surrounding a vehicle; obtaining driver perception data regarding a driver inside the vehicle; determining an integrated risk assessment based on the vehicle data and the driver perception data; and determining an Operational Design Doman (ODD) compliance assessment of the vehicle at least based on the determined integrated risk assessment.

A yaw motion control method for a four-wheel distributed vehicle includes: calculating the steering response of the vehicle in a steady state using a nonlinear vehicle model in reference with an understeering degree while constraining by the limit value of the road surface adhesion condition according to the sideslip angle response and the vertical load change in the steady state, calculating the lateral force response and the self-aligning moment response of the tires in the steady state by a magic tire formula, calculating the required additional yaw moment by using the yaw motion balance equation, reasonably distributing the generalized control force to the four drive motors through the optimization algorithm in combination with the current driving conditions; finally, off-line storing and retrieving the calculation results of the off-line distribution of different vehicle parameters required by different upper layers to distribute the torques to the four drive wheels.

The present disclosure relates to an on-board control system (200) for operating a vehicle (100, 102, 104), the control system (200) comprising processing circuitry (202) and a plurality of sensors (204, 206, 208) arranged with the vehicle (100). The control system (200) employs e.g. a general vehicle control model (M) for operating the vehicle (100, 102, 104), where the vehicle control model (M) is specifically adapted for the vehicle (100, 102, 104) based on an ongoing operation of the vehicle (100, 102, 104). The present disclosure also relates to a corresponding computer implemented method and to a computer program product.

A method autonomously controls a mobility of an automotive apparatus, which mobility is such as to have an influence on the path of the apparatus. The method includes steps of: acquiring parameters relative to the path of the apparatus, and of computing a new control setpoint for the mobility of the apparatus depending on said parameters, this new control setpoint being determined by means of a controller that respects a model that limits the variation in the control setpoint.

According to an aspect, there is provided a computer-implemented method for condition monitoring of a vehicle. The method comprises applying a dynamic model associated with a vehicle (800), the dynamic model having been determined by obtaining status information from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle (200), obtaining, based on vehicle identity information, vehicle dynamics information (202), obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data (204), analyzing behavior of the vehicle based on the status information and the vehicle dynamics information (206), and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data (208); analyzing historical changes in at least one calibration parameter associated with the dynamic model of the vehicle (802); analyzing effects of the three-dimensional road map associated with the roads and the road characteristics data in at least one position on the behavior of the vehicle (804); and determining, based on the analyzed historical changes and the effects, at least one change in at least one vehicle characteristic (806).

An arithmetic operation device for automobiles includes a first arithmetic operation section (301) having a common configuration for a plurality of vehicle models and a second arithmetic operation section (302) having a configuration in accordance with a vehicle model of an automobile. The first arithmetic operation section (301) includes an image processor (311) configured to perform predetermined image processing on an output of a camera installed in the automobile, and a memory (320) configured to store a generated image signal. The second arithmetic operation section (302) includes a processor (340) configured to execute an application in accordance with the vehicle model using the image signal stored in the memory (320) and to transmit an execution result to the first arithmetic operation section (301).

A system includes an inertial navigation system module (INS module) that detects vehicle yaw rates and vehicle lateral speeds, a controller circuit communicatively coupled with the INS module. The controller circuit determines a tire cornering stiffness (C.sub.f, C.sub.r) based on vehicle physical parameters and vehicle dynamic parameters. The controller circuit determines a vehicle moment of inertia (Iz) based on the vehicle physical parameters, the vehicle dynamic parameters, and the tire cornering stiffness (C.sub.f, C.sub.r).

A power distribution control system of a vehicle includes a driving information provider for collecting and providing information required for power distribution control of an engine and a motor in the vehicle; a communication unit for transmitting the information provided by the driving information provider from the vehicle; a cloud server outside the vehicle for selecting and transmitting optimal power distribution control logic data corresponding to a driving situation of the vehicle based on the information provided through the communication unit from the vehicle; and a vehicle controller for performing power distribution control of the engine and the motor based on real-time driving state variable information of the vehicle using the optimal power distribution control logic data received through the communication unit by the vehicle from the cloud server.

The present invention is a method for determining an optimal speed of a first vehicle preceded by a second vehicle. Position, speed and acceleration of the second vehicle are measured in order to determine a trajectory thereof, and a dynamic model of the first vehicle is constructed. The optimal speed is then determined by minimizing the energy consumption of the vehicle by use of the dynamic model by minimization being constrained by the trajectory of the second vehicle.

Systems and methods are provided for vehicle navigation. In one implementation, a processing device may be configured to obtain a planned driving action for accomplishing a navigational goal of a host vehicle; receive sensor data from an environment surrounding the host vehicle; identify a target vehicle moving in the environment and a velocity of the target vehicle; calculate a stopping distance and a predicted trajectory for the target vehicle; calculate a planned trajectory for the host vehicle corresponding to the planned driving action; identify an intersection of the planned trajectory for the host vehicle with the predicted trajectory for the target vehicle; determine a braking action of the host vehicle to comply with a safety requirement; and cause the braking action to be applied to decelerate the host vehicle to change the planned trajectory, until the changed trajectory does not intersect the predicted trajectory of the target vehicle.