Vehicle Trajectory Data

Vehicle trajectory data, encompassing the movement paths of vehicles, is increasingly used to analyze traffic patterns, improve transportation systems, and enhance the safety of autonomous vehicles. Current research focuses on developing machine learning models, including graph neural networks, random forests, and generative adversarial networks, to analyze this data for applications such as traffic incident detection, predicting driver behavior (including lane changes and car-following), and assessing the safety of automated driving systems. These advancements have significant implications for improving traffic flow prediction, enhancing road safety, and developing more robust and reliable autonomous driving technologies.