In autonomous driving, predicting future events in advance and evaluating the
foreseeable risks empowers autonomous vehicles to better plan their actions,
enhancing safety and efficiency on the road. To this end, we propose Drive-WM,
the first driving world model compatible with existing end-to-end planning
models. Through a joint spatial-temporal modeling facilitated by view
factorization, our model generates high-fidelity multiview videos in driving
scenes. Building on its powerful generation ability, we showcase the potential
of applying the world model for safe driving planning for the first time.
Particularly, our Drive-WM enables driving into multiple futures based on
distinct driving maneuvers, and determines the optimal trajectory according to
the image-based rewards. Evaluation on real-world driving datasets verifies
that our method could generate high-quality, consistent, and controllable
multiview videos, opening up possibilities for real-world simulations and safe
planning.

自主驾驶中，通过预测未来事件并评估可预见的风险，使自动驾驶汽车能够更好地规划动作，提高道路安全和效率。我们提出了 Drive-WM，这是第一个与现有端到端规划模型兼容的驾驶世界模型。通过视图因式分解实现的联合时空建模，我们的模型在驾驶场景中生成具有高保真度的多视图视频。基于其强大的生成能力，我们首次展示了将世界模型应用于安全驾驶规划的潜力。特别是，我们的 Drive-WM 能够根据不同的驾驶操纵方式驾驶到多个未来，并根据基于图像的奖励确定最佳轨迹。对真实驾驶数据集的评估验证了我们的方法能够生成高质量、一致性和可控性的多视图视频，为真实世界模拟和安全规划开辟了新的可能性。