Anticipating Traffic Accidents with Adaptive Loss and Large-Scale Incident DB

Abstract

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. The proposed AdaLEA allows us 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, a new Near-miss Incident DataBase (NIDB) that contains an enormous number of traffic near-miss incidents in which the four classes of cyclist, pedestrian, vehicle, and background class are labeled is discussed. The NIDB provides joint estimations of traffic incident anticipation and risk-factor categorization. In our experimental results, we found our proposal achieved the highest scores for anticipation (99.1% mean average precision (mAP) and 4.81 sec anticipation of the average time-to-collision (ATTC), values which are +6.6% better and 2.36 sec faster than previous work) and joint estimation (62.1% (mAP) and 3.65 sec anticipation (ATTC), values which are +4.3% better and 0.70 sec faster than previous work).