Automatic driving solution under multi-target complex traffic scene based on reinforcement learning

Abstract

The invention discloses an automatic driving solution under a multi-target complex traffic scene based on reinforcement learning, which can process all traffic scenes by using a set of reinforcement learning automatic driving modeling method and has better universality. The reinforcement learning comprehensive modeling is based on a traditional reinforcement learning framework, and environment perception information and feature quantity extracted in combination with human knowledge are used as observation space. Model training is based on a time-varying training strategy, and the training speed and the generalization of strategy application are improved. In order to further guarantee the form safety of the vehicle, a dangerous action recognizer based on a long-short term memory (LSTM) network and a rule constraint device based on a human knowledge body are provided, the dangerous action recognizer is sampled from the environment and trained, so that the vehicle has the ability of recognizing dangerous actions and dangerous scenes, and the safety of the vehicle is improved. And rule constraints are designed for specific situations to limit output actions, so that the safety can be greatly improved, the collision frequency is reduced, and the driving safety of the vehicle is guaranteed.

Description

technical field [0001] The invention relates to an automatic driving solution method based on reinforcement learning in multi-objective complex traffic scenarios, belonging to the technical field of automatic driving, and in particular to a general automatic driving algorithm modeling and training scheme based on deep reinforcement learning in multi-objective complex traffic scenarios. Background technique [0002] With the rapid development of the smart car industry and the continuous maturity of autonomous driving technology, driverless technology has become the trend of future vehicle development. At present, the automatic driving system on the ground can reach the L3 level, that is, the vehicle can drive automatically under the condition of driver monitoring, and the driver needs to take over the vehicle in case of emergency. However, one of the important reasons why autonomous driving has not yet achieved complete unmanned driving is that the current rule-based decision...

AI Technical Summary

Problems solved by technology

Therefore, the practical application of deep reinforcement learning in decision-making and control of autonomous driving can be improved to a certain extent to solve the problems of poor versatility, weak generalization, and unimproved safety.

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