[2602.23499] TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving

Computer Science > Robotics arXiv:2602.23499 (cs) [Submitted on 26 Feb 2026] Title:TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving Authors:Tugrul Gorgulu, Atakan Dag, M. Esat Kalfaoglu, Halil Ibrahim Kuru, Baris Can Cam, Ozsel Kilinc View a PDF of the paper titled TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving, by Tugrul Gorgulu and 5 other authors View PDF HTML (experimental) Abstract:Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable. Autonomous driving challenges remain a prominent area of research, requiring further exploration to enhance the perception and planning performance of vehicles. However, existing datasets are often incomplete. For instance, datasets that include perception information generally lack planning data, while planning datasets typically consist of extensive driving sequences where the ego vehicle predominantly drives forward, offering limited behavioral diversity. In addition, many real datasets struggle to evaluate their models, especially for planning tasks, since they lack a proper closed-loop evaluation setup. The CARLA Leaderboard 2.0 challenge, which provides a diverse set of scenarios to address the long-tail problem in autonomous driving, has emerged as a valuable alternative platform for developing perception and planning models in both open-loop and closed-l...