Leveraging spatio-temporal features to forecast time-to-accident

Globally, traffic accidents account for over 3,700 daily deaths, equating to 1.35 million deaths annually. Studies show that collision avoidance systems can significantly reduce the probability and intensity of accidents. Time-to-accident (TTA) is considered the principal parameter for collision avoidance systems allowing for decision-making in traffic, dynamic path planning, and accident mitigation. Despite the importance of TTA, the literature has insufficient research on TTA estimation for traffic scenarios. The majority of recent work focuses on accident anticipation by providing a probabilistic measure of an immediate or future collision. We propose to forecast TTA based on Spatio-temporal features extracted from accident videos obtained via dashboard cameras. Our model can also recognize accident and non-accident scenes with 100% accuracy. Additionally, the impact of spatial resolution and temporal depth on prediction error is analyzed in this work. We implement state-of-the-art video learning architectures and compare the results against static image architectures. Our comprehensive experiments suggest that leveraging Spatio-temporal features is an effective method to estimate TTA. Our best model can estimate the TTA with an average prediction error of 0.30 seconds with a mean prediction horizon of 3.4 seconds.