Intelligent driving automobile obstacle avoidance decision-making method and device

Abstract

The invention provides an intelligent driving automobile obstacle avoidance decision-making method and device. A Gaussian mixture distribution model and a hidden Markov model are models in machine learning and can be automatically executed by a processor and other terminals, and automatic training is performed based on data; compared with rules based on artificial experience and artificial calibration, a confusion matrix and a state transition matrix obtained by the trained Gaussian mixture distribution model and by training of the hidden Markov model are high in accuracy; and therefore, the situation that the accuracy of obstacle avoidance decision making is high on the basis of the confusion matrix and the state transition matrix obtained by the pre-trained Gaussian mixture distributionmodel and by training of the hidden Markov model can be guaranteed, a vehicle can make a correct decision in time, and the driving efficiency and safety are improved.

Description

technical field [0001] The present application relates to the technical field of intelligent transportation, in particular to an obstacle avoidance decision-making method and device for an intelligent driving vehicle. Background technique [0002] When an intelligent driving car is driving, when there are static obstacles or low-speed traffic participants in front of it, if the intelligent driving system has the function of active obstacle avoidance decision-making, the vehicle can avoid the traffic ahead by changing lanes or making detours. Obstacles or traffic participants, in order to improve the efficiency and safety of vehicle driving, so that the vehicle can perform driving tasks more intelligently. [0003] At present, the active obstacle avoidance decision-making method of intelligent driving vehicles is generally based on rules to judge whether to perform active obstacle avoidance. However, the extraction of rules and judgment conditions need to be based on manual e...

AI Technical Summary

Problems solved by technology

[0003] At present, the active obstacle avoidance decision-making method of intelligent driving vehicles is generally based on rules to judge whether to perform active obstacle avoidance. However, the extraction of rules and judgment conditions need to be based on manual experience and a large number of manual calibration work, resulting in the accuracy of decision-making results based on rule judgments. Low reliability, making it difficult for intelligent driving cars to make correct decisions in time, affecting driving efficiency and safety

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