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.
Abstract (translated)
世界模型在自动驾驶中变得至关重要，因为它们可以学习环境随时间如何演变，从而解决现实世界中的长尾挑战。然而，目前的方法将世界模型限制在有限的角色上：尽管这些方法旨在建立统一的架构框架，但仍然将世界预测和运动规划视为独立的过程。为了解决这个问题，我们提出了DriveLaW这一新范式，它统一了视频生成与运动规划过程。通过直接从其视频生成器中注入潜在表示到规划器，DriveLaW确保了高保真未来生成与可靠轨迹规划之间的内在一致性。 具体而言，DriveLaW包括两个核心组件：DriveLaW-Video，这是我们的强大世界模型，它能够生成具有表现力的潜在表示的高保真预测；以及DriveLaW-Act，这是一个扩散规划器，可以从DriveLaW-Video的潜在状态中产生一致且可靠的轨迹。这两个组成部分通过一个三阶段渐进式训练策略进行优化。 我们统一范式的强大之处在于其在视频预测和运动规划任务上都取得了新的最先进的成果。特别是在视频预测方面，DriveLaW不仅显著超越了表现最好的工作，在FID（Frechet Inception Distance）指标上领先33.3%，在FVD（Fréchet Video Distance）指标上领先1.8%；同时还在NAVSIM规划基准测试中创造了新的记录。
URL
https://arxiv.org/abs/2512.23421
