主管:中华人民共和国司法部
主办:司法鉴定科学研究院
ISSN 1671-2072  CN 31-1863/N

中国司法鉴定 ›› 2024 ›› Issue (5): 42-49.DOI: 10.3969/j.issn.1671-2072.2024.05.006

• 专题研讨:智能新能源汽车事故调查与鉴定 • 上一篇    下一篇

基于数据驱动的智能汽车交通事故鉴定方法与应用

李平飞1,2,牟小军1,唐剑军3,刘克亮2,周  鑫2,谭正平1,2,朱欣宇1   

  1. 1. 西华大学 汽车与交通学院,四川 成都 610039; 2. 四川西华交通司法鉴定中心,四川 成都 610039;3. 四川省公安厅交通警察总队,四川 成都 610037
  • 收稿日期:2024-05-31 出版日期:2024-09-15 发布日期:2024-11-28
  • 作者简介:李平飞(1977—),男,副教授,硕士,硕士研究生导师,主要从事智能新能源汽车安全及交通事故深度调查研究。 E-mail:xhlpf12@qq.com
  • 基金资助:
    司法部司法鉴定重点实验室开放课题(KF202211)

Data Driven Method of Intelligent Vehicles Traffic Accident Identification and Its Application

LI Pingfei1,2, MOU Xiaojun1, TANG Jianjun3, LIU Keliang2, ZHOU Xin2, TAN Zhengping1,2, ZHU Xinyu1   

  1. 1. School of Automobile and Transportation, Xihua University, Chengdu 610039, China; 2. Sichuan Xihua Traffic 
    Forensic Appraisal Center, Chengdu 610039, China; 3. Traffic Police Corps of Sichuan Provincial Public 
    Security Bureau, Chengdu 610037, China
  • Received:2024-05-31 Published:2024-09-15 Online:2024-11-28

摘要: 随着我国汽车工业的快速发展,L2级乘用车新车渗透率快速增长。智能汽车较传统汽车的交通事故鉴定更为复杂,给交通事故车辆鉴定及事故调查带来新的挑战。结合交通事故发生的时序进程,提出了一种基于数据驱动的智能汽车交通事故鉴定方法。该方法在事故相关资料基础上,综合智能汽车事故过程中记录的多源异构数据分析事故原因及智能汽车的安全性能。最后以一起典型事故的鉴定,作为智能汽车交通事故调查与鉴定的范例开展讨论。

关键词: 智能汽车, 事故鉴定, 数据驱动, 多源异构

Abstract: With the rapid development of China’s automobile industry, the penetration rate of L2 level passenger cars has increased rapidly. The traffic accidents identification of intelligent vehicles is more complicated than that of traditional vehicles, which brings new challenges to traffic accidents investigation. Combined with the sequential process of traffic accidents, this paper proposed a data driven identification method for intelligent vehicles traffic accidents. Based on accident related data, the method integrates multi-source heterogeneous data that were recorded during the accident process of intelligent vehicles to analyze the cause of the accidents and the safety of intelligent vehicles. Additionally, a typical case was discussed as an example of intelligent vehicles traffic accidents investigation and identification.

Key words:  , intelligent vehicles, accident identification, data driven, multi-source heterogeneou

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