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
DrivingBench is the first benchmark that tests general‑purpose vision‑language models on the task of driving a real Toyota Corolla around a parking‑lot cone course. The models receive live camera frames and issue steering and velocity commands, with inference latency counted as part of the challenge. In tests, only GPT‑6 Astra completed the course, while other models showed limited progress or failed to pass half the course.
By Aditya Ramabadran, Simon Mahns, Tobias Gessler
arXiv:2610.31835v1 Announce Type: new
Abstract: Temporary traffic control redirects vehicles through work zones, requiring automated vehicles to connect an interpretation of the visible devices to a...
By Jiaxi Liu, Keshu Wu, Weizhe Tang, Bofeng Cao, Sikai Chen, Xiaopeng Li, Steven T. Parker, Bin Ran
arXiv:2609.38463v1 Announce Type: cross
Abstract: Autonomous driving planners are typically evaluated using aggregate metrics such as driving score, destination rate, and collision rate, which do not...
By Victoria Smirnova, Viktoriia Zinkovich, Gregorii Bukhtuev, Artem Belyaev, Andrey Kuznetsov, Denis Shepelev, Vlad Shakhuro
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 Endpoint-Constrained Optimization (ECO), a lightweight postprocessing layer that corrects intermediate waypoints of end-to-end driving policies while preserving the predicted endpoint. ECO does not require maps, privileged simulator state, or additional training, and can be applied to a wide range of waypoint-emitting policies. Experiments on two closed-loop simulators show that ECO significantly improves closed-loop performance, achieving top results in the HUGSIM Closed-Loop Driving Challenge and boosting scene scores on AlpaSim.
By Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski, Boris Ivanovic, Kashyap Chitta