arXiv:2607. 23910v1 Announce Type: cross Abstract: Cooperative perception through vehicle-to-everything (V2X) communication can overcome the inherent physical limitations of individual autonomous vehicles, such as occlusions and limited sensor range.
By Goodarz Mehr, Sepideh Gohari, Montasir Abbas, Azim Eskandarian
The paper reviews end‑to‑end autonomous driving (E2E‑AD) training, framing it as a Data‑Strategy‑Platform system. It surveys recent advances in data pipelines, learning paradigms, and training infrastructures, and discusses how these layers interact to influence model performance, robustness, and deployability. The authors highlight current limitations and propose a future vision that prioritizes data value, foundation‑driven generalization, and integrated training‑testing loops for more robust, scalable, and trustworthy autonomous driving systems.
By Chengkai Xu, Yiming Cui, Jiaqi Liu, Yicheng Guo, Cheng Qin, Geyuan Zhang, Xinwei Dong, Shiyu Fang, Peng Hang, Jian Sun
arXiv:2602. 23499v4 Announce Type: replace-cross 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.
By Tugrul Gorgulu, Atakan Dag, M. Esat Kalfaoglu, Halil Ibrahim Kuru, Baris Can Cam, Halil Ibrahim Ozturk, Ozsel Kilinc
arXiv:2508.13977v4 Announce Type: replace
Abstract: Depth estimation is a fundamental component of spatial perception for autonomous driving and other unmanned systems operating in open urban environ...
By Xianda Guo, Ruijun Zhang, Yiqun Duan, Ruilin Wang, Matteo Poggi, Keyuan Zhou, Wenzhao Zheng, Wenke Huang, Gangwei Xu, Yanlun Peng, Yuan Si, Qin Zou
arXiv:2606. 21165v2 Announce Type: replace-cross Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving.
By Juntong Peng, Juanwu Lu, Yupeng Zhou, Can Cui, Yaobin Chen, Ziran Wang
The paper introduces WZPlanner, a new dataset and model for safe autonomous driving in work zones. The dataset, WorkZonePlan, contains over 149,000 synthetic and 5,000 real-world samples with 3D annotations for lane and work zone boundaries, plus 228 evaluation routes in CARLA. The proposed BoundaryFormer (BF) and its enhanced BF++ variants jointly predict lane/work‑zone boundaries and driving trajectories, achieving higher Driving Scores while being significantly smaller than competing models.
By Nishad Sahu (Raj), Changzhong Qian (Raj), Guangzhou Cai (Raj), Shounak Sural (Raj), Ragunathan (Raj), Rajkumar