PSYCH OpenIR
Developing an Interpretable Driver Risk Assessment Model to Increase Driver Awareness Using In-Vehicle Records
Zheng, Min1,2; Zhang, Jingyu1,2
摘要

 

Providing feedback to drivers on their risky driving behaviors is an important method to improve drivers’ awareness in reducing future accidents. However, it is hard to identify risk-prone behaviors and explain them to drivers. In the present study, we used driving log from 103370 electric vehicles equipped with L2-assisted driving functions. We used 28 explainable features to establish a binary classification model of accidents and eight features can be used to establish an acceptable model. Further, we developed an easy-to-understand safety score formula using these eight features. Through this accurate and transparent feedback, we may improve drivers’ safety awareness without undermining their trust in the L2 and higher level automated vehicles. This will not only reduce accidents but enable them to adapt to the development of automated driving technology in a smoother manner.

2023
DOI10.1007/978-3-031-36004-6_18
发表期刊Communications in Computer and Information Science
ISSN1865-0929
卷号1836页码:131-136
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收录类别EI
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文献类型期刊论文
条目标识符https://ir.psych.ac.cn/handle/311026/48165
专题中国科学院心理研究所
作者单位1.Institute of Psychology, Chinese Academy of Sciences, Beijing; 100101, China;
2.Department of Psychology, University of Chinese Academy of Sciences, Beijing; 100049, China
第一作者单位中国科学院心理研究所
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GB/T 7714
Zheng, Min,Zhang, Jingyu. Developing an Interpretable Driver Risk Assessment Model to Increase Driver Awareness Using In-Vehicle Records[J]. Communications in Computer and Information Science,2023,1836:131-136.
APA Zheng, Min,&Zhang, Jingyu.(2023).Developing an Interpretable Driver Risk Assessment Model to Increase Driver Awareness Using In-Vehicle Records.Communications in Computer and Information Science,1836,131-136.
MLA Zheng, Min,et al."Developing an Interpretable Driver Risk Assessment Model to Increase Driver Awareness Using In-Vehicle Records".Communications in Computer and Information Science 1836(2023):131-136.
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