Accident Anticipation via Temporal Occurrence Prediction
Tianhao Zhao, Yiyang Zou, Zihao Mao, Peilun Xiao, Yulin Huang, Hongda Yang, Yuxuan Li, Tracy Li, Guobin Wu, Yutian Lin
Driving accident anticipation aims to predict potential collisions in real time, enabling timely alarms to enhance road safety. Existing methods typically predict frame-level anomaly scores as risk indicators. However, these approaches suffer from inconsistent supervision signals because driving risks evolve progressively rather than abruptly, and risk assessment inherently involves human subjectivity. To address this limitation, we propose a novel paradigm that directly predicts the probability of an accident occurring at multiple future timestamps (0.1s–2.0s), offering more precise supervision and improved interpretability. Our framework employs a snippet encoder to capture spatiotemporal dynamics and a Transformer-based decoder to simultaneously estimate accident probabilities across different time steps. Furthermore, we introduce a refined evaluation protocol that measures recall rate and Time-to-Accident (TTA) only under acceptable false alarm rates, ensuring practical applicability in the real world. Experiments demonstrate that our method achieves superior performance in both recall and TTA, validating its effectiveness for real-world accident anticipation.