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
arXiv:2608. 03521v1 Announce Type: cross Abstract: Forecasting precise future motion of surrounding agents is essential for reliable autonomous vehicles.
By Xiucong Zhao, Jindong Tian, Hao Miao
arXiv:2601.01762v4 Announce Type: replace-cross
Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes....
By Yanhao Wu, Haoyang Zhang, Fei He, Rui Wu, Yanhu Shan, Congpei Qiu, Liang Gao, Wei Ke, Tong Zhang
The paper proposes a new training dataset that generates more informative positive and negative samples for trajectory scoring in autonomous driving. By perturbing logged human trajectories laterally toward the drivable boundary and longitudinally toward a leading vehicle, the dataset provides richer supervision than the planner’s default proposal pool. Using a transformer-based scorer trained on this dataset, the authors achieve improved EPDMS scores on two frozen planners, DiffusionDrive and MeanFuser, when evaluated on the NAVSIM navtrain dataset.
By Yaguang Li, Jiaru Zhang, Chuheng Wei, Can Cui, Ziran Wang
PlanWAM introduces a Planning‑Shaped World Action Model that tailors future‑state representations to be most useful for trajectory planning in end‑to‑end autonomous driving. It compresses historical observations with a Temporal Register Pyramid, shapes a future latent representation using a privileged posterior branch guided by planning objectives, and distills this into a prior branch that predicts the representation from history alone. The resulting foresighted planning context yields state‑of‑the‑art performance on NAVSIM and HUGSIM benchmarks.
By Jinchang Xu, Hongda Yu, Fengwei Dong, Wenhui Huang, Xi Wei, Yongzhi Liu, Sunan Zhang, Jirao Wang, Chen Lv, Bingbing Li, Guodong Yin, Weichao Zhuang
arXiv:2610.09695v1 Announce Type: cross
Abstract: Visual foundation models (VFMs) are increasingly integrated into end-to-end autonomous driving for their powerful representations, yet it remains unc...
By Zihao Zhang, Haochen Tian, Tianyu Li, Changhui Jing, Jingliang He, Naisheng Ye, Ziyuan Pu, Zhenjie Yang
arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.
By Amirhosein Chahe, Tyler Naes, Jovin D'sa, Faizan M. Tariq, Sangjae Bae, Lifeng Zhou, David Isele
arXiv:2606. 06014v1 Announce Type: new Abstract: Latent world models (LWMs) have strengthened end-to-end autonomous driving by forecasting compact scene dynamics for downstream planning.
By Xiaoyun Qiu, Jingtao He, Yijie Chen, Yusong Huang, Haotian Wang, Yixuan Wang, Xinhu Zheng
arXiv:2607. 07844v1 Announce Type: cross Abstract: While closed-loop motion planners trained on large-scale, object-level datasets, e.
By Alessandro Canevaro, Hang Yu, Julian Schmidt, Peizheng Li, Silvan Lindner, Wilhelm Stork, Georg Martius, Julian Jordan
arXiv:2608.20974v1 Announce Type: cross
Abstract: Video Joint Embedding Predictive Architecture (V-JEPA) learns powerful spatiotemporal representations from video through self-supervised latent featu...
By Xinlin Wang, Yujiao Xiang, Yuheng Zhou, Jingqi Wang, Minqing Huang, Jiajie Huang, Dongxu Wei, Tingguang Zhou, Xiyang Wang, Gong Chen, Zhi Xu, Feiyang Tan, Hangning Zhou, Mu Yang
FPD-Drive is an end‑to‑end autonomous driving planner that uses future‑privileged multi‑target distillation to transfer diverse, high‑quality trajectory targets from a teacher model with access to future observations to a student policy. It also introduces safety‑aware score correction to improve candidate selection by adjusting rankings between safe and unsafe trajectories. The approach achieves state‑of‑the‑art performance on NAVSIM v1 and v2 benchmarks and demonstrates strong zero‑shot closed‑loop results on HUGSIM.
By Li-Heng Chen, Qifeng Chen, Chengwei Wu, Yingjie Zhang, Sheng Yang, Hongbo Fu, Shaoqing Ren
arXiv:2606. 07464v1 Announce Type: cross Abstract: Monolithic vision-action models represent an emerging paradigm in autonomous driving.
By Zhixuan Liang, Yuxiao Chen, Yurong You, Peter Karkus, Wenhao Ding, Boyi Li, Alexander Popov, Yan Wang, Maximilian Igl, Yiming Li, Danfei Xu, Nikolai Smolyanskiy, Boris Ivanovic, Ping Luo, Marco Pavone