基于自然驾驶数据的典型风险场景致因分析

doi:10.3969/j.issn.1674-0696.2022.01.03

摘要/Abstract

摘要： 为探究城市交通中驾驶风险工况的主要影响因素以及因素之间的相互作用关系，在哈尔滨城市道路上开展了驾驶试验，采集自然驾驶数据并提取了20个危险工况的影响因素，构建了改进的DEMATEL-ISM模型，对各个致因的相关关系和重要程度进行量化处理；针对行人和非机动车横向冲突、前车变道冲突两个典型风险场景，结合模型输出的层次结构图进行复杂网络度值分析。根据获取的深层诱导因素、直接影响因素和核心影响因素归纳了两个风险场景的形成链路。研究表明：两类风险场景的深层诱导因素主要为道路状态和交通管制的交叉作用；直接影响因素、核心影响因素和场景形成链路存在显著差异。

关键词: 交通工程；自然驾驶；危险工况；影响因素分析；Apriori算法；DEMATEL-ISM模型

Abstract: To explore the main influencing factors and the interaction of factors in dangerous driving conditions of urban traffic, the driving tests were carried out on urban roads in Harbin and the naturalistic driving data were collected. The influencing factors of 20 dangerous working conditions were extracted, and an improved DEMATEL-ISM model was constructed to quantify the correlation and importance of each cause. For the two typical risk scenarios of pedestrian and non-motor vehicle lateral conflict and front vehicle lane change conflict, combined with the hierarchical structure diagram output by the model, the complexity network value of the influencing factors of the two risk scenarios was analyzed. The deep inducing factors, direct influencing factors and core influencing factors of the two kinds of critical scenarios were obtained, and the formation links of the two dangerous scenarios were summarized. Through comparison, it is found that the deep inducing factors of the two types of dangerous conditions are mainly the interaction of road state and traffic control, while there are significant differences in direct influencing factors, core influencing factors and scene forming links. It is found that the in-depth inducing factors of the two types of dangerous conditions are mainly the intersection of road conditions and traffic control, but the direct influence factors, core influence factors and scene formation links show significant differences.

Key words: traffic engineering; naturalistic driving; critical scenarios; influencing factors analysis; Apriori algorithm; DEMATEL-ISM model

中图分类号:

U491.2

冯树民，吴迪，孙雅丽. 基于自然驾驶数据的典型风险场景致因分析[J]. 重庆交通大学学报（自然科学版）, 2022, 41(01): 13-21.

FENG Shumin, WU Di, SUN Yali. Cause Analysis of Typical Critical Scenarios Based on Naturalistic Driving Data[J]. Journal of Chongqing Jiaotong University(Natural Science), 2022, 41(01): 13-21.

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