The present invention discloses an intelligent vehicle platoon lane change performance evaluation method. First, an intelligent vehicle platoon lane change performance test scenario is established; secondly, a three-degree of freedom nonlinear dynamics model is established according to motion characteristics of intelligent vehicles in a platoon lane change process; further, an improved adaptive unscented Kalman filter algorithm is utilized to perform filter estimation on state variables of positions and velocities of platoon vehicles; and finally, based on accurately recursive vehicle motion state parameters, evaluation indexes for platoon lane change performance are proposed and quantified, and an evaluation system for platoon lane change performance is constructed. According to the method proposed in the present invention, the problem of lacking platoon lane change performance quantitative evaluation at present is solved, vehicle motion state parameters can be measured in a high-precision and comprehensive manner, multi-dimensional platoon lane change performance evaluation indexes are quantified and output, and comprehensive, accurate, and reliable scientific quantitative evaluation for platoon lane change performance is achieved.

A computer implemented method for controlling a vehicle includes obtaining a value of the mass of the vehicle, receiving a plurality of time sequential measured first values of one or more further state parameters, calculating a first plurality of time sequential values of the vehicle mass, including a first calculated mass value, using the plurality of measured first values of the one or more further state parameters, the non-linear model, and an extended Kalman filter with a first filter tuning, with the obtained mass value as a start value, receiving a plurality of time sequential measured second values of the one or more of the further state parameters, and calculating a second plurality of time sequential values of the vehicle mass, including a second calculated mass value, using the plurality of measured second values of the one or more further state parameters, the non-linear model, and an extended Kalman filter with a second filter tuning, with the first calculated mass value as a start value, wherein the second filter tuning is made less aggressive than the first filter tuning.

The present invention discloses an intelligent vehicle platoon lane change performance evaluation method. First, an intelligent vehicle platoon lane change performance test scenario is established; secondly, a three-degree of freedom nonlinear dynamics model is established according to motion characteristics of intelligent vehicles in a platoon lane change process; further, an improved adaptive unscented Kalman filter algorithm is utilized to perform filter estimation on state variables of positions and velocities of platoon vehicles; and finally, based on accurately recursive vehicle motion state parameters, evaluation indexes for platoon lane change performance are proposed and quantified, and an evaluation system for platoon lane change performance is constructed. According to the method proposed in the present invention, the problem of lacking platoon lane change performance quantitative evaluation at present is solved, vehicle motion state parameters can be measured in a high-precision and comprehensive manner, multi-dimensional platoon lane change performance evaluation indexes are quantified and output, and comprehensive, accurate, and reliable scientific quantitative evaluation for platoon lane change performance is achieved.

A vehicle including a Global Positioning System (GPS) sensor, an Inertial Measurement Unit (IMU), and an Advanced Driver Assistance System (ADAS) is described. Operating the vehicle includes determining, via the GPS sensor, first parameters associated with a velocity, a position, and a course, and determining, via the IMU, second parameters associated with acceleration and angular velocity. Roll and pitch parameters are determined based upon the first and second parameters. A first vehicle velocity vector is determined based upon the roll and pitch parameters, the first parameters, and the second parameters; and a second vehicle velocity vector is determined based upon the roll and pitch parameters, road surface friction coefficient, angular velocity, road wheel angles and the first vehicle velocity vector. A final vehicle velocity vector is determined based upon fusion of the first and second vehicle velocity vectors. The vehicle is controlled based upon the final vehicle velocity vector.

A method for controlling an automated or autonomous locomotive device, including automatically ascertaining a deviation from a predefined trajectory, the deviation requiring a return of the locomotive device to the predefined trajectory; automatically calculating a jerk as an input variable, as a function of the deviation from the predefined trajectory; automatically calculating an unconstrained correcting variable for the return to the predefined trajectory, as a function of a weighted sum that includes a weighted summand of the input variable and a weighted summand of the state for the return path; automatically calculating a constrained correcting variable regarding the jerk; the unconstrained correcting variable being manipulated via a cascade that includes multiple stages having one saturation function per stage; integrating the constrained correcting variable, to obtain a constrained return trajectory to the predefined trajectory; automatically steering the locomotive device to the predefined trajectory by way of the constrained return trajectory.

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 vehicle including a Global Positioning System (GPS) sensor, an Inertial Measurement Unit (IMU), and an Advanced Driver Assistance System (ADAS) is described. Operating the vehicle includes determining, via the GPS sensor, first parameters associated with a velocity, a position, and a course, and determining, via the IMU, second parameters associated with acceleration and angular velocity. Roll and pitch parameters are determined based upon the first and second parameters. A first vehicle velocity vector is determined based upon the roll and pitch parameters, the first parameters, and the second parameters; and a second vehicle velocity vector is determined based upon the roll and pitch parameters, road surface friction coefficient, angular velocity, road wheel angles and the first vehicle velocity vector. A final vehicle velocity vector is determined based upon fusion of the first and second vehicle velocity vectors. The vehicle is controlled based upon the final vehicle velocity vector.

A method for monitoring vehicle inertia parameters in real-time includes receiving at least one lateral dynamic value. The method also includes calculating at least one vehicle inertia parameter value using the at least one lateral dynamic value. The method also include determining a difference between the calculated at least one vehicle inertia parameter value and a corresponding baseline vehicle inertia parameter value. The method also includes, based on a comparison between the difference between the calculated at least one vehicle inertia parameter value and the corresponding baseline vehicle inertia parameter value and a threshold, selectively controlling at least one vehicle operation based on the calculated at least one vehicle inertia parameter value.

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 remote driving device configured to remotely operate a vehicle includes a remote operation device, a reaction force unit, a receiver, and a processor. The remote operation device is operated by an operator in order to remotely operate the vehicle. The reaction force unit is configured to generate an operation reaction force to be applied to the remote operation device. The receiver is configured to receive a parameter affecting vehicle characteristics of the vehicle from the vehicle. The processor is configured to control the reaction force unit so as to generate a magnitude of the operation reaction force according to the received parameter.