Accident Detection

Accident detection research focuses on automatically identifying traffic accidents from video data, primarily to improve autonomous vehicle safety and enhance smart city traffic management. Current approaches leverage deep learning, employing architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, often incorporating multi-modal data (e.g., RGB images and optical flow) and advanced attention mechanisms to improve accuracy and robustness in complex scenarios. These advancements aim to create real-time, reliable accident detection systems capable of reducing accident severity and improving emergency response times, impacting both the development of safer autonomous vehicles and the efficiency of urban infrastructure.