Realistic scene-level multi-agent motion simulations are crucial for
developing and evaluating self-driving algorithms. However, most existing works
focus on generating trajectories for a certain single agent type, and typically
ignore the consistency of generated trajectories. In this paper, we propose a
novel framework based on diffusion models, called SceneDM, to generate joint
and consistent future motions of all the agents, including vehicles, bicycles,
pedestrians, etc., in a scene. To enhance the consistency of the generated
trajectories, we resort to a new Transformer-based network to effectively
handle agent-agent interactions in the inverse process of motion diffusion. In
consideration of the smoothness of agent trajectories, we further design a
simple yet effective consistent diffusion approach, to improve the model in
exploiting short-term temporal dependencies. Furthermore, a scene-level scoring
function is attached to evaluate the safety and road-adherence of the generated
agent's motions and help filter out unrealistic simulations. Finally, SceneDM
achieves state-of-the-art results on the Waymo Sim Agents Benchmark. Project
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基于扩散模型的新框架 SceneDM 旨在生成场景中各类代理（车辆、自行车、行人等）的联合一致未来行动，并通过基于 Transformer 的网络处理代理之间的相互作用，设计了简单而有效的一致扩散方法以提高模型性能，并附加了场景级评分函数来评估生成代理行动的安全性和道路遵守性，帮助过滤出不切实际的模拟。该框架在 Waymo Sim Agents Benchmark 上取得了最先进的结果。