Accident Datasets

Accident datasets are crucial for developing and evaluating machine learning models aimed at improving safety in various domains, from autonomous driving to workplace safety. Current research focuses on addressing challenges like imbalanced data through techniques like oversampling and extending existing frameworks (e.g., the accident triangle), and leveraging advanced architectures such as graph neural networks and multimodal models incorporating vision and language to enhance accident prediction and understanding. These efforts are significant because improved accident prediction and analysis can lead to more effective safety interventions, resource allocation, and the development of safer systems across numerous sectors.