Motorcade collaborative driving lane changing control method

Abstract

The invention relates to a motorcade collaborative driving lane changing control method. The method comprises the steps that a vehicle lane changing behavior rule base is established in advance in a controller of an automatic driving vehicle; when the automatic driving vehicle forms a team for cruising and encounters a triggering reason of a vehicle lane changing event, the vehicle lane changing event is triggered directly or after a motorcade splits the event; after the vehicle lane changing event is triggered, the controller of the automatic driving vehicle controls the automatic driving vehicle to execute a vehicle lane changing driving behavior based on a vehicle lane changing behavior rule base rule; and when the vehicle lane changing driving behavior is executed, the controller obtains pose information of the automatic driving vehicle, predicts to obtain the motion action of the automatic driving vehicle at the next moment, judges whether the motion action of the automatic driving vehicle at the next moment is matched with the vehicle lane changing behavior rule base or not, and if so, iteratively updates the vehicle lane changing driving behavior rule in the vehicle lane changing behavior rule base by utilizing the pose information of the current automatic driving vehicle. According to the invention, the optimal control effect on vehicle lane changing can be realized.

Description

technical field [0001] The invention belongs to the technical field of vehicle automatic driving, and in particular relates to a lane-changing control method for team cooperative driving. Background technique [0002] Self-driving vehicles are not only important electromechanical products, but also high-tech carriers such as new energy, new materials, and new equipment, covering basic science and common technical issues in the intersecting fields of environmental perception, planning and decision-making, information communication, and automatic control. Among them, planning and decision-making is responsible for generating safe and reasonable driving behavior like a skilled driver, so as to control the vehicle automatically. [0003] With the development of artificial intelligence technology represented by deep learning and machine learning, the "end-to-end" driving decision-making method, which simulates the driver to directly generate driving decision-making instructions b...

AI Technical Summary

Problems solved by technology

However, the offline training of this method in the real scene requires a large enough sample, and the provided samples often contain concerns that are irrelevant to driving decisions, and the interpretability is poor; at the same time, the test in the simulation scene cannot be directly used in the actual environment, lacking Practicality

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