基于危险场景聚类分析的前车随机运动状态预测研究*

doi:10.19562/j.chinasae.qcgc.2020.07.001

Abstract

Abstract: Due to complex and changeable road situation in China, the construction of a suitable test scenario plays a decisive role in the validity of intelligent driving system test and evaluation. Based on the actual road traffic environment in China, typical scenarios with Chinese characteristics are extracted and the data fragments of risk working condition are selected from the naturalistic driving data. Then, the scenario feature elements of the comprehensive tests of intelligent driving system are extracted, and three types of typical risk scenarios are obtained by clustering analysis. Then the stochastic motion of the vehicle driven by the human driver in front of the subject vehicle is featured by the Markov chain theory and the subject vehicle data in each scenario obtained by clustering is used as the historical working condition data of the preceding vehicle. The Markov chain transfer probability is concluded and learned, and the state of the future time is predicted by Markov chain Monte Carlo simulation. Based on this, the stochastic motion prediction model of the preceding vehicle in the risk scenarios is obtained. The validity of the proposed model is verified by comparing the original working condition data, which effectively solves the problem that the preceding vehicle data is in accurate due to the low accuracy of the acquisition equipment and the stochastic motion of the preceding vehicle driven by the human driver cannot be accurately featured in test scenarios.

Key words: intelligent drive, naturalistic driving data, clustering risk scenarios, Markov Chain Monte Carlo, prediction

Guo Jinghua, Li Keqiang, Wang Jin, Chen Tao, Li Wenchang, Wang Ban. Study on Prediction of Preceding Vehicle's Stochastic Motion Based on Risk Scenarios Clustering Analysis[J].Automotive Engineering, 2020, 42(7): 847-853.

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Study on Prediction of Preceding Vehicle's Stochastic Motion Based on Risk Scenarios Clustering Analysis

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