arXiv:2601. 21288v2 Announce Type: replace Abstract: Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and planning in this domain.
By Weitong Lian, Zecong Tang, Haoran Li, Tianjian Gao, Yifei Wang, Zixu Wang, Lingyi Meng, Tengju Ru, Zhejun Cui, Yichen Zhu, Hangshuo Cao, Qi Kang, Tianxing Chen, Kaixuan Wang, Yu Zhang
arXiv:2606. 28758v1 Announce Type: cross Abstract: Predicting future states is essential for autonomous agents, yet current Vision-Language-Action (VLA) models fundamentally lack this capability, relying instead on reactive perception-action mapping.
By Bohao Zhao, Chengrui Wei, Guangfeng Jiang, Ruixin Liu, Xuejie Lv, Liu Liang, Sutao Deng, Xiuyang Fan, Pengkun Zheng, Jinyun Zhou, Rui Guo, Hanpeng Liu, Yutong Zheng, Yi Guo, Xinlong Zheng, Qingyu Luo, Zhuangzhuang Ding, Yu Zhang, Hang Zhang, Xianming Liu
arXiv:2608. 12854v1 Announce Type: cross Abstract: Autonomous driving requires planning under both semantic constraints and predictive dynamics.
By Bing Zhan, Shuyao Shang, Jiahao Gu, Shuo Lu, Yuan Xu, Zhao Wang, Yida Wang, Xueyang Zhang, Kun Zhan, Lue Fan, Zhaoxiang Zhang
arXiv:2607. 04179v1 Announce Type: cross Abstract: End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving.
By Zhaohong Liu, Hao Ye, Xianlin Zhang, Mengshi Qi
arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.
By Yining Xing, Zehong Ke, Zhiyuan Liu, Yanbo Jiang, Wenhao Yu, Jianqiang Wang
The paper introduces LaPla, a Vision‑Language‑Action framework that uses a latent‑aligned planning approach to convert discrete semantic reasoning into continuous, physics‑constrained driving actions. It employs a residual VQ‑VAE to encode vehicle kinematics into a structured latent space, then projects multimodal inputs—images, past actions, and text—directly into this latent space, allowing a frozen decoder to generate physically plausible trajectories without quantization errors. Experiments on nuScenes and NVIDIA AlpaSim show LaPla reduces long‑horizon L2 error by 15.52% and improves closed‑loop success rates by 33.34 percentage points while cutting inference latency.
By Ruoyu Yao, Yusen Xie, Qingzhao Liu, Pei Liu, Zewei Yang, Yipeng Zhu, Xiaolong Wang, Jun Ma