An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model

The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.

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Publication Details

Journal
Traffic Injury Prevention
Published
2024-10-02
DOI
https://doi.org/10.1080/15389588.2024.2399301
Citations
7
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
1.42

Funders

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article

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model

Zhongxiang Feng, Dianchen Zhu, Zhipeng Huang, Xinyi Wei et al.
7 citations
Traffic Injury Prevention
Autonomous Vehicle Technology and Safety
1.42
article

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model

Zhongxiang Feng, Dianchen Zhu, Zhipeng Huang, Xinyi Wei, Yu Bi
article en
7 citations

Abstract

The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.

Traffic Injury PreventionVol. 26(2)
Hefei University of Technology (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 19%
Autonomous Vehicle Technology and Safety
1.42
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An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model — Zhongxiang Feng, Dianchen Zhu, et al. · Traffic Injury Prevention (2024) | TGRS Research Map | TGRS