PV-WM is a history‑only world model that jointly predicts pedestrian root motion, 15‑joint articulation, and vehicle kinematic states in a synchronized heterogeneous state. It uses recurrent updates to generate pedestrian and vehicle motion chunks, reconstructing vehicle boxes from predicted center, heading, and observed extent, and recomputes pedestrian‑vehicle geometry after each transition. Compared to a one‑shot predictor, PV‑WM reduces Root ADE by 12.7% and MPJPE by 14.8%, and across 824 Waymo contexts it lowers Root ADE by 5.2%, MPJPE by 7.6%, P‑V distance error by 11.9%, and oriented‑box closest‑approach error by 5.8%, while using 57.1% fewer parameters, 96.5% fewer FLOPs, and 25.5% lower p95 latency.
By Haozhuang Chi, Jingsong Liang, Ziying Song, Lei Yang, Shihao Li, Haoruo Zhang, Chen Lv
arXiv:2606. 18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian.
By Yuxuan Xie, Nicolas Pugeault, Chongfeng Wei, Hubert P. H. Shum, Edmond S. L. Ho
arXiv:2606. 15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics.
By Maonan Wang, Zhengyan Huang, Kemou Jiang, Yuhang Fu, Jiayue Zhu, Yuxin Cai, Xingchen Zou, Qiaosheng Zhang, Yi Yu, Ding Wang, Xi Chen, Ben M. Chen, Yuxuan Liang, Zhiyong Cui, Man On Pun, Yirong Chen
arXiv:2608.28656v1 Announce Type: cross
Abstract: Behavior-cloned Vision-Language-Action (VLA) driving policies struggle with rare rule-governed maneuvers at signalized intersections. Braking and lau...
By Bala Murali Manoghar Sai Sudhakar, Sourab Bapu Sridhar, Sandipan Das, Rahul Ahuja, Meda Lazar, Ashish Garg, Pratik Likhar, Senthil Yogamani
arXiv:2607. 09740v1 Announce Type: new Abstract: Safe motion planning in advanced driver-assistance systems and autonomous vehicles requires an accurate understanding of how the surrounding traffic scene is likely to evolve.
By Joshua Kofi Asamoah, Blessing Agyei Kyem, Eugene Denteh, Armstrong Aboah
End-to-end autonomous driving models are now able to navigate complex road scenarios, mapping raw sensor observations directly to observed paths for open-loop evaluation and often effective driving in closed-loop evaluation. Yet the internal logic of these safety-critical systems remains largely opaque, due to the complexity of traffic scenes.