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汽车安全与节能学报 ›› 2020, Vol. 11 ›› Issue (2): 220-226.DOI: 10.3969/j.issn.1674-8484.2020.02.009

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用于自动驾驶汽车的汽车—骑车人事故场景分析

韩大双,马志雄,朱西产 * ,曾宇凡   

  1. (同济大学 汽车学院,上海 201800,中国)
  • 收稿日期:2019-05-19 出版日期:2020-06-30 发布日期:2020-06-30
  • 通讯作者: 朱西产,教授。E-mail :xczhu@163.com。
  • 作者简介:第一作者 / First author : 韩大双 (1994—),男 ( 汉 ),山东,硕士研究生。E-mail :hands@tongji.edu.cn。
  • 基金资助:
    “十三五”国家重点研发计划 (2016YFB0100904-2)。

Car-cyclist accident scene analysis for autopilot vehicles

HAN Dashuang, MA Zhixiong, ZHU Xichan* ,ZENG Yufan   

  1. (School of Automotive Studies, Tongji University, Shanghai 201804, China)
  • Received:2019-05-19 Online:2020-06-30 Published:2020-06-30

摘要: 为在汽车—骑车人场景下测试评价自动驾驶汽车的安全性,建立了汽车—骑车人事故场景测 试工况库和相应的测试工况集评价模型。利用江苏省某市的交通事故数据库和中国国家车辆事故深度 调查体系(NAIS)数据库,筛选出了116 起汽车—骑车人碰撞事故;使用分类树的方法,提取了7 种 事故场景;根据道路与环境参数,生成该场景总体的测试工况集。使用层次分析法 (AHP) 和模糊综 合评价法,建立了汽车与骑车人碰撞事故场景的评价模型。结果表明:作为一种典型的中国事故测试 工况,当晴天且该路段无遮挡,乘用车与两轮车同向行驶时,两轮车横穿具有最高评分和最大危险度。

关键词: 自动驾驶汽车, 事故评价, 汽车—骑车人场景, 数据库, 国家车辆事故深度调查体系(NAIS), 层次分析法 (AHP)

Abstract: A vehicle-ride-accident scenario test-condition-library and the corresponding test condition set evaluation model were established to test and evaluate the safety of autopilot in vehicle rider scenarios. 116 vehicle cyclist crashes were screened out. Seven accident scenarios were extracted by using the method of classification tree based on the traffic accident database of a city in Jiangsu Province of China, and based on the National Vehicle Accident In-depth Investigation System (NAIS) database of China. An overall test condition set of the scenarios was generated according to the road and environmental parameters. An evaluation model of vehicle and cyclist crash scene is established by using an analytic hierarchy process (AHP) and a fuzzy comprehensive evaluation method. The results show that as a typical type of accident test condition in China, two-wheeled-vehicle crossing has the highest score and the highest risk on an unobstructed road section in a sunny day with both a passenger-car and a two-wheeled-vehicle driving in a same direction.

Key words: automatic driving vehicles, accident evaluation, vehicle rider scenarios, database, National Automobile Accident In-Depth Investigation System (NAIS), analytic hierarchy process (AHP)

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