用拓扑地图增强扩散模型，让车辆轨迹更准更自然。
TopoDiffuser: A Diffusion-Based Multimodal Trajectory Prediction Model with Topometric Maps
- 将道路拓扑结构融入扩散模型去噪过程，生成符合道路几何的轨迹。
- 在KITTI数据集上优于现有方法，轨迹几何一致性更强。
- 适合自动驾驶路径预测研究者参考，尤其关注多模态融合与道路约束。
本文提出TopoDiffuser，一种基于扩散模型的多模态轨迹预测框架，通过引入拓扑地图（topometric maps）生成准确、多样且符合道路规则的未来运动预测。该方法将拓扑地图中的结构线索嵌入条件扩散模型的去噪过程中，使生成轨迹自然遵循道路几何，无需依赖显式约束。一个多模态条件编码器融合了LiDAR观测、历史运动和路线信息，构建统一的鸟瞰图（BEV）表征。在KITTI基准上的大量实验表明，TopoDiffuser优于当前最优方法，同时保持良好的几何一致性。消融实验验证了各输入模态的贡献，以及去噪步数和轨迹样本数量的影响。为支持后续研究，代码已公开于https://github.com/EI-Nav/TopoDiffuser。
原文摘要 · Abstract (English)
This paper introduces TopoDiffuser, a diffusion-based framework for multimodal trajectory prediction that incorporates topometric maps to generate accurate, diverse, and road-compliant future motion forecasts. By embedding structural cues from topometric maps into the denoising process of a conditional diffusion model, the proposed approach enables trajectory generation that naturally adheres to road geometry without relying on explicit constraints. A multimodal conditioning encoder fuses LiDAR observations, historical motion, and route information into a unified bird's-eye-view (BEV) representation. Extensive experiments on the KITTI benchmark demonstrate that TopoDiffuser outperforms state-of-the-art methods, while maintaining strong geometric consistency. Ablation studies further validate the contribution of each input modality, as well as the impact of denoising steps and the number of trajectory samples. To support future research, we publicly release our code at https://github.com/EI-Nav/TopoDiffuser.
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