In this paper, we propose a novel approach for traffic accident anticipation through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a large-scale self-annotated incident database for anticipation. The proposed AdaLEA allows a model to gradually learn an earlier anticipation as training progresses. The loss function adaptively assigns penalty weights depending on how early the model can anticipate a traffic accident at each epoch. Additionally, we construct a Near-miss Incident DataBase for anticipation. This database contains an enormous number of traffic near-miss incident videos and annotations for detail evaluation of two tasks, risk anticipation and risk-factor anticipation. In our experimental results, we found our proposal achieved the highest scores for risk anticipation (+6.6 better on mean average precision (mAP) and 2.36 sec earlier than previous work on the average time-to-collision (ATTC)) and risk-factor anticipation (+4.3 better on mAP and 0.70 sec earlier than previous work on ATTC).
Dashcam Accident Dataset (DAD) @cite_0 contains accidental events on the collected data. However, it is not large enough (there are only @math -order accident videos, which are 5 seconds each) to optimize a high-level model, and many of accidents in DAD are caused between others (e.g., other motorcycle-other vehicle), not own vehicle and other that should be avoided by own vehicle. Thus, there is an urgent need for a collection of large-scale incident scenes with annotations to ensure that a self-driving car can learn how to safely navigate dangerous situations. In this paper, we constructed a novel database that contains a large-number of near-miss traffic incidents with detailed annotations, especially for risk anticipation task that is the one of the key problem to the implementation of self-driving car.
@cite_0
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We propose a Dynamic-Spatial-Attention (DSA) Recurrent Neural Network (RNN) for anticipating accidents in dashcam videos (Fig. 1). Our DSA-RNN learns to (1) distribute soft-attention to candidate objects dynamically to gather subtle cues and (2) model the temporal dependencies of all cues to robustly anticipate an accident. Anticipating accidents is much less addressed than anticipating events such as changing a lane, making a turn, etc., since accidents are rare to be observed and can happen in many different ways mostly in a sudden. To overcome these challenges, we (1) utilize state-of-the-art object detector [3] to detect candidate objects, and (2) incorporate full-frame and object-based appearance and motion features in our model. We also harvest a diverse dataset of 678 dashcam accident videos on the web (Fig. 3). The dataset is unique, since various accidents (e.g., a motorbike hits a car, a car hits another car, etc.) occur in all videos. We manually mark the time-location of accidents and use them as supervision to train and evaluate our method. We show that our method anticipates accidents about 2 s before they occur with 80 recall and 56.14 precision. Most importantly, it achieves the highest mean average precision (74.35 ) outperforming other baselines without attention or RNN.