VCDSet: A New Vehicle Collision Dataset In Asia Countries For Anticipating Accidents

Abstract

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The safety of autonomous vehicles is a crucial concern in the field of transportation. In recent years, a number of research approaches have been proposed to address this issue, including car accident analysis, obstacle detection, lane recognition, and sign recognition. However, there is often the possibility of detecting clues that precede a collision. To better understand driving behavior and enhance the driving experience of autonomous vehicles, a number of large-scale datasets have been created by various research groups. However, none of these datasets specifically focus on risky driving behaviors, which can directly lead to accidents. By detecting risky driving behaviors in advance, it is possible to provide additional response time for autonomous vehicles. While a few car collision datasets do exist, the unique environment in Asian countries, which often involves a high number of motorcycles or bikes, can lead to a wide range of vehicle accidents. In this paper, we introduce a new dataset, named VCDSet, which consists of 603 dashcam videos of car accidents that were collected in Asian countries and include extensive annotations, including weather, road conditions, accident types, and the time at which the accident occurred. We also propose a preliminary approach for anticipating car accidents using our VCDSet and demonstrate that our method can effectively increase response time before a collision occurs.