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Road injuries are rated among the top 10 causes of death by the World Health Organization, and the only one that is not a disease. The total economic cost of motor vehicle crashes in the United States was estimated to be $242 billion a year. This study examines multiple factors of accidents simultaneously with a goal of generating an interpretable model that can predict the occurrence of an accident given road conditions and driver behavior. The study compared 4 machine learning and deep learning modeling techniques on a dataset of 7707 trips collected by the Second Strategic Highway Research Program. A gradient boosted model was found to be most accurate and interpretable in accident prediction. This modeling technique also allows us to rank the feature importance of the factors in the model. The study finds that driver behavior, pre-incident maneuvers and secondary task duration are the most important variables in the predictive model. Using these conclusions will allow us to perform more work to infer these accident causes directly from vehicle sensor data in the future.
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Dilemma Zone: A Comprehensive Study of Influential Factors and Behavior Analysis
In this paper, we examine the problem of the Dilemma Zone (DZ) in depth, weaving together the various influences that span the environment, the ego-vehicle, and ultimately the characteristics of the driver. Driver behavior in dilemma zone situations is crucial, and more research is urgently needed in this area. The journey through various modeling approaches and data acquisition techniques sheds new light on driver behavior within the dilemma zone context. Our thorough examination of the current research landscape has revealed that several significant areas remain overlooked. As well as the dynamic impact of vehicles, vehicle interactions, and a strong tendency to over-rely on infrastructure information, there are also concerns about the lack of comprehensive evaluation tools. However, we do not see these gaps as stumbling blocks, but rather as steppingstones for future research opportunities. A more focused study of cooperative solutions is required considering the potential of personalized modeling, the untapped power of machine learning techniques, and the importance of personalized modeling. It is our hope that by embracing innovative approaches that can capture and simulate personalized behavioral data using “everything-in-the-loop” simulations, future research endeavors will be guided. To effectively mitigate the DZ problem, we also point out the research gaps and opportunities for further research in the DZ.
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- Award ID(s):
- 2152258
- PAR ID:
- 10510372
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7066-9
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Location:
- Riverside, CA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/FISTS60717.2024.10485546
