Automatic driving control method and device, vehicle and storage medium

Abstract

The invention discloses an automatic driving control method and device, a vehicle and a storage medium. The method comprises the following steps: determining a planned path and a planned speed of automatic driving according to a first objective function under a natural coordinate system; determining a target trajectory of automatic driving according to the planned path, the planned speed and a second target function; and solving a control sequence according to the target trajectory, and controlling automatic driving of the vehicle according to the control sequence. According to the technical scheme, on the basis of the planned path and the planned speed, the target trajectory of automatic driving of the vehicle is accurately determined according to the target function, then automatic driving of the vehicle is controlled according to the target trajectory, and the reliability of automatic driving is improved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of vehicle control, and in particular to an automatic driving control method, device, vehicle and storage medium. Background technique [0002] Automobiles are an indispensable means of transportation in people's life and work. They are becoming more and more intelligent, especially the automatic driving function, which brings great convenience to people. In the trajectory planning process of automatic driving, the parameters in the vehicle kinematics and dynamics models are uncertain, and in the complex and random road environment, there will be other motor vehicles, pedestrians, etc., the behavior of these agents is random It brings great difficulty and challenge to the autonomous decision-making of vehicles. [0003] At present, some intelligent decision-making methods are applied to autonomous driving, such as planning and control of autonomous driving based on deep learning. This ...

AI Technical Summary

Problems solved by technology

In the trajectory planning process of automatic driving, the parameters in the vehicle kinematics and dynamics models are uncertain, and in the complex and random road environment, there will be other motor vehicles, pedestrians, etc., the behavior of these agents is random It brings great difficulty and challenge to the autonomous decision-making of vehicles

[0003] At present, some intelligent decision-making methods are applied to autonomous driving, such as planning and control of autonomous driving based on deep learning. This method can theoretically fit any function, but requires a large number of artificial labels and samples, which cannot be realized in the short term; For example, planning and control based on reinforcement learning, setting reward and punishment functions based on behaviorism, and optimizing decision-making through continuous trial and error. However, in automatic driving decision-making, some mistakes are unacceptable and cannot be tried.

Therefore, the current planning and control of autonomous driving is still in relatively simple working conditions, and reliable autonomous decision-making cannot be achieved.

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