Driving Data

Driving data research focuses on creating and utilizing high-quality datasets to improve autonomous vehicle (AV) development and safety. Current efforts concentrate on generating synthetic data using generative models like diffusion transformers and GANs to address the limitations of real-world data collection, and on developing efficient methods for querying and analyzing large, labeled time series datasets to extract meaningful driving scenarios. This research is crucial for validating AV performance in diverse and challenging situations, improving the safety and reliability of autonomous driving systems, and ultimately enabling safer and more efficient transportation.