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.

In a vehicle control device, a switching hyperplane generation unit generates a switching hyperplane based on a travel state of a vehicle and cornering stiffness dependent on a travel surface state as a state of a road surface on which the vehicle travels. A deviation computation unit calculates a deviation between a target trajectory and an actual trajectory of the vehicle. A state estimation unit estimates a state to be controlled of the vehicle based on the deviation calculated by the deviation computation unit. A target steering angle and acceleration/deceleration computation unit calculates a target steering angle and a target acceleration/deceleration rate of the vehicle based on the switching hyperplane generated by the switching hyperplane generation unit and an estimated state as the state estimated by the state estimation unit.

Techniques and methods for testing a structural integrity of a vehicle. For instance, the vehicle may use suspension(s) to lift corners of the vehicle, where lifting the corners creates a torsion force in the body of the vehicle. The vehicle may then use sensor(s) to determine a deflection that is caused by the torsion force. Next, the vehicle may determine a stiffness associated with the body of the vehicle using the force that was applied to lift the corners and the deflection. After determining the stiffness, the vehicle may compare the stiffness to a baseline stiffness in order to determine whether there is a problem with the structural integrity of the vehicle. The baseline stiffness may include s standard stiffness or be based on a previous test of the vehicle. If the vehicle determines that there is no problem, then the vehicle may continue to operate as intended.

Motion control systems and methods are provided in a vehicle. In one embodiment, a motion control system includes a controller. The controller is configured to: receive target trajectory data associated with an upcoming trajectory of the autonomous vehicle; determine a yaw rate reference and a relative heading reference associated with the upcoming target trajectory based on a numerical integration of the target trajectory data; and control a trajectory of the autonomous vehicle based on the yaw rate reference and the relative heading reference.

A method of calculation a vehicle load comprising calculating a first vehicle load value based at least on air pressures in air springs and height data of suspension of a vehicle axle, determining a second vehicle load value based on a change of track width of the vehicle axle, and calculating the vehicle load based on the first vehicle load value and the second vehicle load value.

The invention relates to an autonomous driving control method, an autonomous driving control system, and a vehicle. The autonomous driving control method includes: estimating motion control capacities of a vehicle on the basis of performance parameters and/or structure parameters of the vehicle; grading autonomous driving functions of the vehicle on the basis of the motion control capacities; and providing the graded autonomous driving functions on the basis of a state of the vehicle. The autonomous driving control method of the invention can provide different grades of autonomous driving functions in different vehicle states, thereby improving driving experience.

Aspects and implementations of the present disclosure relate to performance and safety improvements for autonomous trucking systems, such as mitigation of blind spots in the field of view of a sensing system of an autonomous vehicle, using shielding by other vehicles in adverse weather conditions, and deploying a cooperative expansion of the sensing field of view using external sensing systems.

Aspects and implementations of the present disclosure relate to performance and safety improvements for autonomous trucking systems, such as reactive suspensions for maximizing aerodynamic performance and minimizing mechanical impact from road imperfections, automated placement of emergency signaling devices, and techniques of enhanced illumination of stopped and stranded vehicles.

A method, a device, a storage medium and a vehicle for vehicle rollover prevention warning, wherein the method for vehicle rollover prevention warning includes: collecting vehicle body rollover state parameters; calculating a lateral-load transfer rate of the vehicle according to the collected vehicle body rollover state parameters and a preset load transfer rate threshold model comprising the centrifugal force rollover moment of the sprung mass; and determining whether the vehicle has the risk of rollover or not according to the calculated lateral-load transfer rate and the preset rollover threshold. According to the technical solution provided by the invention, the load transfer rate threshold model based on the centrifugal force rollover moment of the sprung mass can simulate the actual rollover state of a vehicle more truly, with a more accurate state indication effect and a high warning accuracy.

An apparatus of determining a target steering angle, may include: a feedforward steering angle calculator configured for determining a feed forward steering angle reflecting a yaw moment generated by torque vectoring during turning driving of an electric vehicle in autonomous driving; and an adder configured for obtaining a target steering angle by adding the determined feedforward steering angle to a feedback steering angle, the feedback steering angle being a steering angle measured through a steering angle sensor.