车辆典型危险行驶状态识别与检测研究进展

doi:10.16265/j.cnki.issn1003-3033.2017.10.006

中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (10): 32-37.doi: 10.16265/j.cnki.issn1003-3033.2017.10.006

车辆典型危险行驶状态识别与检测研究进展

刘通¹, 付锐^1,2 教授, 张士伟¹, 邓明阳¹

1 长安大学汽车学院,陕西西安 710064;
2 长安大学汽车运输安全保障技术交通行业重点实验室,陕西西安 710064

收稿日期:2017-07-09 修回日期:2017-09-10 出版日期:2017-10-20 发布日期:2020-11-05
作者简介:刘通 (1989—),男,山西怀仁人,博士研究生,研究方向为人-车-路系统安全、车辆状态识别、驾驶员类型划分。E-mail:liutong@chd.edu.cn。
基金资助:
国家自然科学基金资助(61374196);教育部长江学者和创新团队支持计划项目(IRT1286)。

Progress in research on identification and detection of vehicle typical hazardous driving states

LIU Tong¹, FU Rui^1,2, ZHANG Shiwei¹, DENG Mingyang¹

1 School of Automobile, Chang'an University, Xi'an Shaanxi 710064, China;
2 Key Laboratory of Automobile Transportation Safety Technology, Ministry of Transport, Chang'an University, Xi'an Shaanxi 710064, China

Received:2017-07-09 Revised:2017-09-10 Online:2017-10-20 Published:2020-11-05

摘要/Abstract

摘要： 为识别和检测车辆在行驶过程中可能出现的危险状态,及时给予驾驶员反馈和预警,使车辆始终保持安全的运行状态,重点从车辆典型危险行驶状态的识别、检测2个方面,梳理纵向及横向危险行驶状态及其表征参数,总结主要的识别与检测方法,并展望其未来研究趋势。结果表明:不同文献对驾驶事件的危险阈值划分原则差异较大,尚未形成统一的标准;隐马尔科夫模型 (HMM) 对危险驾驶事件的识别准确率相对较高;车辆典型危险行驶状态的几种识别方法各有优点和缺点,基于便携式设备和多传感器数据的识别方法相对较优,且基于多传感器数据融合的车辆危险行驶状态识别与检测是未来的重点研究方向。

关键词: 驾驶行为, 危险行驶状态, 危险识别, 车辆检测系统, 阈值划分, 隐马尔科夫模型(HMM)

Abstract: Progress in research on identification and detection of vehicle typical hazardous driving states was reviewed. Longitudinal and horizontal hazardous driving states and their characteristic parameters were examined, methods of identification and detection of the states were summarized. Future research trends were prospected. The results show that there are some differences in the range of thresholds for driving events in different literatures and there is still lacking of a uniform standard, that HMM has higher accuracy in the hazardous driving events identification, that available identification methods of typical hazardous driving states of vehicle have their own advantages and disadvantages, the methods based on portable devices and multi-sensors data are relatively superior, and that identification and detection of vehicle hazardous driving states based on the fusion data of multi-sensors will be the future research direction.

Key words: driving behavior, hazardous driving state, hazard identification, vehicle detection system, threshold division, hidden Markov model (HMM)

中图分类号:

刘通, 付锐, 张士伟, 邓明阳. 车辆典型危险行驶状态识别与检测研究进展[J]. 中国安全科学学报, 2017, 27(10): 32-37.

LIU Tong, FU Rui, ZHANG Shiwei, DENG Mingyang. Progress in research on identification and detection of vehicle typical hazardous driving states[J]. China Safety Science Journal, 2017, 27(10): 32-37.

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车辆典型危险行驶状态识别与检测研究进展

Progress in research on identification and detection of vehicle typical hazardous driving states

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