S2Planner is a trajectory planner for autonomous driving that fuses data from three front-facing cameras, ego‑motion history, and the current driving command. It uses a fine‑tuned DINOv3 backbone with a Spatial Tuning Adapter to generate multi‑scale image features, which are refined by a coarse‑to‑fine decoder employing trajectory self‑attention and camera‑projected cross‑attention. The key contribution lies in integrating ego‑conditioned trajectory initialization with iterative, geometry‑guided sampling of multi‑scale image features, rather than introducing a new visual backbone or attention operator.
By Zhaowei Lu, Liguo Zhou, Yujie Guo, Lei Yu, Alois Knoll
arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.
By Chen Yang, Yuhao Wei, Ze Xu, Ziheng Zou, Shuang Liang, Delin Ouyang, Lingfeng Qi, Jie Li, Guofa Li
arXiv:2606. 17294v1 Announce Type: cross Abstract: Vision Navigation Foundation Models (VNMs) promise end-to-end learned navigation policies capable of zero-shot deployment across diverse embodiments and environments.
By Maeva Guerrier, Koki Kobayashi, Simon Roy, Jana Pavlasek, Giovanni Beltrame
arXiv:2605. 06317v4 Announce Type: replace-cross Abstract: Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulation and limits efficiency.
By Dijia Zhan, Jinyi Li, Chenxi Zheng, Shaoyu Huang, Yong Li, Jie Tang, Xuemiao Xu
arXiv:2606. 07244v1 Announce Type: cross Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions while navigating in real-world-like environments.
By Haoxiang Shi, Xiang Deng, Haoyu Zhang, Qiaohui Chu, Yaowei Wang, Liqiang Nie
MM-Future is a world-action model for autonomous driving that generates multiple paired scene-action hypotheses and captures bidirectional interaction within each pair. It initializes each hypothesis from a structured action prior and an independent future scene source, then co-evolves them using a modality-aware diffusion Transformer. The model compresses multi-view video into planning-oriented MM-Tokens and uses a future-conditioned proposal scorer to rank trajectory candidates, achieving strong performance on NAVSIM and HUGSIM benchmarks.
By Shuai Liu, Hechangle Gong, Hao Jiang, Runlin He, Junxiang Zhan, Kai Huang, Sheng Yang, Shaoqing Ren