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http://dx.doi.org/10.12815/kits.2017.16.5.96

Evaluation of Accident Prevention Performance of Vision and Radar Sensor for Major Accident Scenarios in Intersection  

Kim, Yeeun (Dept. of Civil & Environmental Eng., KAIST)
Tak, Sehyun (Dept. of Civil & Environmental Eng., KAIST)
Kim, Jeongyun (Dept. of Civil & Environmental Eng., KAIST)
Yeo, Hwasoo (Dept. of Civil & Environmental Eng., KAIST)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.16, no.5, 2017 , pp. 96-108 More about this Journal
Abstract
The current collision warning and avoidance system(CWAS) is one of the representative Advanced Driver Assistance Systems (ADAS) that significantly contributes to improve the safety performance of a vehicle and mitigate the severity of an accident. However, current CWAS mainly have focused on preventing a forward collision in an uninterrupted flow, and the prevention performance near intersections and other various types of accident scenarios are not extensively studied. In this paper, the safety performance of Vision-Sensor (VS) and Radar-Sensor(RS) - based collision warning systems are evaluated near an intersection area with the data from Naturalistic Driving Study(NDS) of Second Strategic Highway Research Program(SHRP2). Based on the VS and RS data, we newly derived sixteen vehicle-to-vehicle accident scenarios near an intersection. Then, we evaluated the detection performance of VS and RS within the derived scenarios. The results showed that VS and RS can prevent an accident in limited situations due to their restrained field-of-view. With an accident prevention rate of 0.7, VS and RS can prevent an accident in five and four scenarios, respectively. For an efficient accident prevention, a different system that can detect vehicles'movement with longer range than VS and RS is required as well as an algorithm that can predict the future movement of other vehicles. In order to further improve the safety performance of CWAS near intersection areas, a communication-based collision warning system such as integration algorithm of data from infrastructure and in-vehicle sensor shall be developed.
Keywords
Intersection Accident; Cooperative Intelligent Transportation System; Advanced Driver Assistance System; Accident Prevention; Accident Scenario;
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