融合物理规律的扩散模型，提升自动驾驶轨迹预测精度与合理性。
Physics-informed Diffusion Mamba Transformer for Real-world Driving
- 用Mamba+注意力机制增强扩散过程，捕捉长期时序依赖
- 引入能量守恒物理约束，使预测轨迹更符合真实运动规律
- 在主流驾驶数据集上表现优于现有方法，适合高安全需求场景
自动驾驶系统需要能建模未来运动不确定性、同时尊重复杂时序依赖和物理规律的轨迹规划器。尽管基于扩散的生成模型擅长捕捉多模态分布，但常忽视长期序列上下文和领域特定物理先验。本文提出两项关键创新：首先，设计了融合Mamba与注意力机制的扩散变换器架构，有效整合传感器流与历史运动信息的时序上下文；其次，构建了端口-哈密顿神经网络模块，将基于能量的物理约束无缝融入扩散模型，提升轨迹预测的一致性与可解释性。在标准自动驾驶基准上的大量实验表明，该统一框架在预测准确性、物理合理性及鲁棒性方面显著优于当前最优基线，推动了安全可靠的运动规划发展。
原文摘要 · Abstract (English)
Autonomous driving systems demand trajectory planners that not only model the inherent uncertainty of future motions but also respect complex temporal dependencies and underlying physical laws. While diffusion-based generative models excel at capturing multi-modal distributions, they often fail to incorporate long-term sequential contexts and domain-specific physical priors. In this work, we bridge these gaps with two key innovations. First, we introduce a Diffusion Mamba Transformer architecture that embeds mamba and attention into the diffusion process, enabling more effective aggregation of sequential input contexts from sensor streams and past motion histories. Second, we design a Port-Hamiltonian Neural Network module that seamlessly integrates energy-based physical constraints into the diffusion model, thereby enhancing trajectory predictions with both consistency and interpretability. Extensive evaluations on standard autonomous driving benchmarks demonstrate that our unified framework significantly outperforms state-of-the-art baselines in predictive accuracy, physical plausibility, and robustness, thereby advancing safe and reliable motion planning.
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