  Numerous Deep Learning and sensor-based models have been developed to detect
potential accidents with an autonomous vehicle. However, a self-driving car
needs to be able to detect accidents between other vehicles in its path and
take appropriate actions such as to slow down or stop and inform the concerned
authorities. In this paper, we propose a novel support system for self-driving
cars that detects vehicular accidents through a dashboard camera. The system
leverages the Mask R-CNN framework for vehicle detection and a centroid
tracking algorithm to track the detected vehicle. Additionally, the framework
calculates various parameters such as speed, acceleration, and trajectory to
determine whether an accident has occurred between any of the tracked vehicles.
The framework has been tested on a custom dataset of dashcam footage and
achieves a high accident detection rate while maintaining a low false alarm
rate.
