The paper introduces a low‑cost, open experimental platform for end‑to‑end autonomous driving on miniature Ackermann vehicles, combining a physical car, printed track, data collection tools, trajectory registration, and a Webots digital twin. It implements command‑conditioned behavior cloning, achieving a mean cross‑track error of 6.1 cm on the real vehicle and demonstrating the impact of camera field of view in simulation. Using synthetic data from the digital twin and a sim‑to‑real image translator, a higher‑capacity policy trained on both synthetic and real data completes all four track routes, outperforming the baseline trained only on real data.
By Gustavo Claudio Karl Couto, Eric Aislan Antonelo, Gabriel George Zipperer
The paper introduces a low‑cost, open experimental platform for end‑to‑end autonomous driving on miniature Ackermann vehicles. It combines a physical vehicle, a printed urban track, data collection tools, trajectory registration, and a Webots digital twin to link simulation and real‑world experiments. Using command‑conditioned behavior cloning, the authors demonstrate that a neural policy can follow lanes with a mean cross‑track error of 6.1 cm, and that synthetic data plus a sim‑to‑real image translator improves performance on all track routes.
The paper explores Behavior Cloning (BC) for autonomous driving using bird's‑eye view (BEV) inputs in simulated urban settings. It introduces a weighted BC approach that employs a kernel density estimator to adjust training sample weights, emphasizing rare but critical actions like stopping at red lights and accelerating at green lights. Experiments in the CARLA simulator show that this weighted method improves route completion and generalizes better to unseen test environments compared to standard BC.
By Eric Aislan Antonelo, Gustavo Claudio Karl Couto, Christian M\"oller, Pedro Henrique Fernandes
The paper introduces DriveMLP, a lightweight MLP policy for autonomous driving that is trained using an Expert‑Guided RL (EGRL) framework. EGRL leverages reinforcement learning solely as an optimization tool, guided by a trajectory‑distance reward derived from logged expert data, and the study shows that the training strategy itself yields significant performance gains over model complexity. DriveMLP achieves an 86.51 score on the nuPlan Val14 Reactive benchmark, surpassing the previous best IL planner by 3.2 points while offering more than 15× faster inference.
By Alessandro Canevaro, Julian Schmidt, Hang Yu, Ye Jin, Sajad Marvi, Georg Martius, Julian Jordan
OPTED is a method for on‑policy fine‑tuning of end‑to‑end driving models that separates reinforcement learning from the policy update. A privileged teacher trained with RL on vectorized inputs (HD‑maps and bounding boxes) supervises the pre‑trained student during closed‑loop post‑training. Applied to the camera‑based models TransFuser and VaVAM in AlpaSim, OPTED boosts driving scores by 1.6× and 9.5×, respectively, while requiring roughly three orders of magnitude fewer simulator interactions than direct RL post‑training.
By Damiano Da Col, Maximilian Igl, Peter Karkus, Kashyap Chitta, Boris Ivanovic, Marco Pavone, Konrad Schindler, Christos Sakaridis
The paper introduces Closed-Loop Aware Planning for Autonomous Driving (CLAP‑AD), a new planning head that learns a driving path via imitation while independently scoring candidate speeds using a learned function guided by safety constraints. Unlike traditional open‑loop imitation learning, CLAP‑AD allows explicit test‑time control over speed selection without requiring speed‑conditioned training. Experiments on Bench2Drive‑Speed, Bench2Drive, and NAVSIM v2 demonstrate significant improvements in speed‑adherence and overall driving performance compared to state‑of‑the‑art baselines.
By Emad Aghajanzadeh, Thomas Schwarzfischer, Marc Kaufeld, Moritz Thoma, Ryan P\'egoud, Manoj Rohit Vemparala, Pierpaolo Mori, Nael Fasfous, Johannes Betz, Daniel Mueller-Gritschneder, Ulf Schlichtmann