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.
Authors
- Zhongxiang Feng (ORCID: https://orcid.org/0000-0002-4388-0787)
- Dianchen Zhu (ORCID: https://orcid.org/0000-0001-8614-5582)
- Zhipeng Huang (ORCID: https://orcid.org/0000-0001-5958-538X)
- Xinyi Wei (ORCID: https://orcid.org/0000-0002-9853-1816)
- Yu Bi
Institutions
- Hefei University of Technology (CN)
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
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities