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Citation |
Han I. Traffic Injury Prev. 2020; ePub(ePub): ePub. |
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Affiliation |
Department of Mechanical and Design Engineering, Hongik University, Sejong, South Korea. |
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Copyright |
(Copyright © 2020, Informa - Taylor and Francis Group) |
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DOI |
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PMID |
32401549 |
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Abstract |
Objective: Intersection collision is the most common type of vehicle accidents, and it is more complicated and has more variables compared to straight road collisions. Intersection collisions are also a major obstacle to practical application of self-driving technology. This calls for more studies and development of Intersection-Advanced Driver Assistance System (I-ADAS) which can be applied to various scenarios of intersection collisions.Method: In this study, NHTSA FARS and NASS-CDS DB from the period of 2013-2015 were used to analyze the circumstances and severity of damage in intersection collisions. With these analysis results, 17 possible vehicle collision scenarios were established based on the travel directions and relative positions of the two vehicles. The 17 accident scenarios were categorized into nine groups based on the travel direction and relative positions, and accident characteristics of each group, such as the severity of injuries, were analyzed.Results: Based on these characteristics, a method of qualitatively predicting and avoiding an intersection collision accident using a black box camera mounted on vehicles is introduced.Conclusion: When classifying the collision types, it was done with the consideration of making car-mounted camera-based intersection accident prediction and prevention possible. The intersection accident scenarios deduced for the purpose of the development of I-ADAS and self-driving system were analyzed regarding the severity of injuries and other factors. Based on these factors, possible methods to predict and prevent intersection accidents by using video footages and other data from the black box installed on cars were simply suggested. Language: en |
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Keywords |
Intersection collisions; accident prediction; accident scenario; advanced driver assistance systems; characteristics analysis |