Roundabout Traffic Conflict Dataset

Roundabout traffic conflict datasets are crucial for developing and evaluating safer and more efficient autonomous driving systems, particularly in complex multi-agent scenarios. Current research focuses on using these datasets to train and test various machine learning models, including reinforcement learning algorithms (e.g., DDPG, PPO, TRPO) and graph neural networks, for predicting conflicts, optimizing vehicle trajectories, and improving driver behavior prediction. This work aims to enhance both autonomous and human-driven vehicle safety and efficiency at roundabouts, contributing significantly to advancements in intelligent transportation systems and autonomous vehicle technology. The availability of high-quality, annotated datasets like ROCO is essential for driving progress in this area.