{"ID":2824320,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23421","arxiv_id":"2512.23421","title":"DriveLaW:Unifying Planning and Video Generation in a Latent Driving World","abstract":"World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approaches relegate world models to limited roles: they operate within ostensibly unified architectures that still keep world prediction and motion planning as decoupled processes. To bridge this gap, we propose DriveLaW, a novel paradigm that unifies video generation and motion planning. By directly injecting the latent representation from its video generator into the planner, DriveLaW ensures inherent consistency between high-fidelity future generation and reliable trajectory planning. Specifically, DriveLaW consists of two core components: DriveLaW-Video, our powerful world model that generates high-fidelity forecasting with expressive latent representations, and DriveLaW-Act, a diffusion planner that generates consistent and reliable trajectories from the latent of DriveLaW-Video, with both components optimized by a three-stage progressive training strategy. The power of our unified paradigm is demonstrated by new state-of-the-art results across both tasks. DriveLaW not only advances video prediction significantly, surpassing best-performing work by 33.3% in FID and 1.8% in FVD, but also achieves a new record on the NAVSIM planning benchmark.","short_abstract":"World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approaches relegate world models to limited roles: they operate within ostensibly unified architectures that still keep world prediction and motio...","url_abs":"https://arxiv.org/abs/2512.23421","url_pdf":"https://arxiv.org/pdf/2512.23421v3","authors":"[\"Tianze Xia\",\"Yongkang Li\",\"Lijun Zhou\",\"Jingfeng Yao\",\"Kaixin Xiong\",\"Haiyang Sun\",\"Bing Wang\",\"Kun Ma\",\"Guang Chen\",\"Hangjun Ye\",\"Wenyu Liu\",\"Xinggang Wang\"]","published":"2025-12-29T12:32:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}