Accident Prediction

Accident prediction research aims to proactively mitigate traffic accidents using various data sources and advanced algorithms. Current efforts focus on developing sophisticated models, including graph neural networks and transformer-based architectures, that integrate multimodal data (e.g., video, sensor data, weather, road network information) to improve the accuracy and timeliness of predictions, often focusing on both when and where accidents might occur. This field is crucial for enhancing autonomous driving safety, informing infrastructure improvements, and ultimately reducing the global burden of traffic accidents.