SceneDM: Consistent Diffusion Models for Coherent Multi-agent Trajectory Generation

Zhiming Guo, Xing Gao, Jianlan Zhou, Xinyu Cai, Xuemeng Yang, Licheng Wen, Xiao Sun

Abstract: Realistic multi-agent motion simulations are essential for the advancement of self-driving algorithms. However, the majority of existing works tend to overlook the kinematic realism of the simulated motions. In this paper, we present SceneDM, a novel consistent diffusion model designed to jointly generate consistent and realistic motions for all types of agents within a traffic scene. To employ temporal dependencies and improve the kinematic realism of the generated motions, we introduce an innovative constructive noise pattern alongside smoothing regularization techniques integrated into the framework of the diffusion model. Moreover, the inference procedure of this model is tailored to effectively ensure local temporal consistency. Furthermore, a scene-level scoring function is incorporated to evaluate the safety and road adherence of the generated agents’ motions, helping to filter out unrealistic simulations. Through empirical validation in the Waymo Sim Agents task, we substantiate the effectiveness of SceneDM in improving the smoothness and realism of generated agent trajectories. The project webpage is available at https://alperen-hub.github.io/SceneDM.

External IDs:dblp:conf/case/GuoGZCYWS25