The paper introduces CADET, a training‑free framework for auditing, benchmarking, and repairing spurious reliance in pretrained end‑to‑end autonomous‑driving planners. It addresses the problem that such planners often learn statistical shortcuts—associating co‑occurring scene elements with driving decisions—rather than causal variables, which undermines reliability in rare scenarios. CADET can detect and correct these causal confusions without retraining the model or updating its parameters.
By Zikun Guo, Minglan Chen, Jinyou Zhai, Rongjin Zou
arXiv:2606. 31106v1 Announce Type: cross Abstract: Large-scale datasets and fast simulators have enabled improvements in driving policies that appear safe and robust, yet strong performance in nominal scenarios can still mask flawed reasoning and unsafe heuristics.
By Hyeonchang Jeon, Kyungbeom Kim, Eugene Vinitsky, Kyung-Joong Kim
End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving. This class of models includes both Vision-Language-Action models and trajectory-generative planners.
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
arXiv:2603. 14354v3 Announce Type: replace-cross Abstract: End-to-End autonomous driving (E2E-AD) systems face challenges in lifelong learning, including catastrophic forgetting, difficulty in knowledge transfer across diverse scenarios, and spurious correlations between unobservable confounders and true driving intents.
By Jiayuan Du, Yuebing Song, Yiming Zhao, Xianghui Pan, Jiawei Lian, Yuchu Lu, Liuyi Wang, Chengju Liu, Qijun Chen
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