A proactive crash risk prediction framework for lane-changing behavior incorporating individual driving styles
Driving style may have an important effect on traffic safety. Proactive crash risk prediction for lane-changing behaviors incorporating individual driving styles can help drivers make safe lane-changing decisions. However, the interaction between driving styles and lane-changing risk is still not fully understood, making it difficult for advanced driver-assistance systems (ADASs) to provide personalized lane-changing risk information services. This paper proposes a personalized risk lane-changing prediction framework that considers driving style. Several driving volatility indices based on vehicle interactive features have been proposed, and a dynamic clustering method is developed to determine the best identification time window and methods of driving style. The Light Gradient Boosting Machine (LightGBM) based on Shapley additive explanation is used to predict lane-changing risk for cautious, normal, and aggressive drivers and to analyze their risk factors. The highD trajectory dataset is used to evaluate the proposed framework. The obtained results show that i) spectral clustering and a time window of 3 s can accurately identify driving styles during the lane-changing intention process; ii) the LightGBM algorithm outperforms other machine learning methods in personalized lane-changing risk prediction; iii) aggressive drivers seek more individual driving freedom than cautious and normal drivers and tend to ignore the state of the car behind them in the target lane, with a greater lane-changing risk. The research conclusion can provide basic support for the development and application of personalized lane-changing warning systems in ADASs.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
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Supplemental Notes:
- © 2023 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
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Authors:
- Zhang, Yunchao
- Chen, Yanyan
- Gu, Xin
- 0000-0002-7841-302X
- Sze, N N
- Huang, Jianling
- Publication Date: 2023-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 107072
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Serial:
- Accident Analysis & Prevention
- Volume: 188
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0001-4575
- Serial URL: http://www.sciencedirect.com/science/journal/00014575
Subject/Index Terms
- TRT Terms: Crash risk forecasting; Driver performance; Driver support systems; Lane changing; Predictive models; Risk taking
- Subject Areas: Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01882319
- Record Type: Publication
- Files: TRIS
- Created Date: May 22 2023 1:28PM