Method for generating lane changing rule of automatic driving automobile

Abstract

The invention provides a method for generating a lane changing rule of an automatic driving automobile. The method comprises the following steps: S1, acquiring traffic data of a selected road area; S2, screening from the traffic data according to lane changing behavior influence factors to obtain sample data; S3, calculating the grey entropy correlation degree of each lane changing behavior influence factor according to the sample data, and obtaining the condition attribute of the lane changing behavior; and S4, constructing a lane changing behavior decision tree based on the information gainand gain rate of the condition attribute of the lane changing behavior, and generating a lane changing rule according to the lane changing behavior decision tree. According to the method, the machinelearning-decision tree-learning is effectively utilized to simulate the human driving behavior, so that the uninterpretability of the driving behavior learned by the neural network algorithm is avoided, and meanwhile, the over-constraint phenomenon and the under-constraint phenomenon of the lane changing behavior caused by manually setting rules are also avoided.

Description

Technical field [0001] The technical field of automobile automatic driving of the present invention particularly relates to a method for generating lane-changing rules for an automatic driving automobile. Background technique [0002] As one of the important tools of modern transportation, cars not only bring convenience to people's lives, but also cause traffic jams and frequent traffic accidents. Studies have shown that human factors such as inattention or insufficient driving experience are the main causes of traffic accidents. Self-driving cars liberate the driver from the traditional "person-vehicle-road" closed-loop control system and fundamentally solve the above problems. Compared with the driver operating the car, the self-driving car effectively avoids inattention, and can respond quickly to dangerous scenes, thereby effectively improving the safety of the vehicle and the transportation efficiency of the transportation system. [0003] Self-driving cars use cameras, lid...

AI Technical Summary

Problems solved by technology

There are relatively many existing studies on the analysis of lane-changing execution and lane-changing safety, but relatively few studies on the judgment of lane-changing intentions

At the same time, the current lane-changing behavior decision-making rules are mainly based on the driver's experience, and the corresponding rules and parameters are set subjectively, resulting in serious over-constraint phenomenon, which is quite different from the lane-changing behavior of human drivers.

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