Vehicle control method, chassis domain controller and intelligent driving vehicle

Abstract

The application provides a vehicle control method, a chassis domain controller, and an intelligent driving vehicle. The method is applied to the chassis domain controller, including: obtaining vehicle state data; determining the fifteen-degree-of-freedom dynamic model data of the vehicle according to the vehicle state data ; Obtain the first motion boundary, which is determined by the external intelligent driving domain controller based on the driving road data; determine the second dynamic boundary of the car according to the first motion boundary and the fifteen-degree-of-freedom dynamic model data; The second power boundary and the preset corresponding relationship determine the third execution boundary, so that the actuator on the vehicle controls the operation of the vehicle according to the vehicle control data within the third execution boundary. In this way, the current driving environment can be fully utilized under the condition of ensuring driving stability, and the disadvantage of adopting an overly conservative control strategy can be overcome. And it can easily and accurately obtain the execution amount boundary of the actuator, which alleviates the accuracy of the current control algorithm and the problem of computing resources.

Description

technical field [0001] The present application relates to the technical field of smart cars, in particular, to a car control method, a chassis domain controller and a smart driving car. Background technique [0002] The application of artificial intelligence technology in the automotive industry has begun to land, and more and more sensor technologies, signal processing technologies, and artificial intelligence algorithms have entered the stage of actual vehicle application. For example, the application of sensor information fusion technology such as camera vision technology and lidar in vehicle driving environment and state perception. In order to judge the state of the vehicle itself and the driving environment more efficiently and accurately, hardware devices for special computing of real-time high-dimensional digital signals such as computer vision and radar point clouds are used in intelligent driving technology to support more The application of sensing devices in sma...

AI Technical Summary

Problems solved by technology

[0003] However, the current intelligent driving algorithm has idealized assumptions about driving conditions, which limits the scope of application and cannot be widely used.

However, an overly conservative decision-making assumption can guarantee the safety of the algorithm decision-making, but it will cause the actual stable driving area of ​​the vehicle to not be fully utilized, and it is difficult to cope with extreme working conditions; Driving, but for an uncertain traffic environment (such as a sudden encounter with a road surface), the decision-making control based only on the external information of the vehicle will cause the vehicle to exceed the stability boundary

At the same time, due to the idealized assumption of the vehicle form state, the control decision boundary is large and the boundary is blurred, so it will inevitably lead to high computational cost of deep learning algorithms, large amount of data required, and poor actual fitting effect And other issues

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