This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model, a
novel network architecture for highway trajectory prediction using diffusion
models. The proposed model ensures the drivability of the predicted trajectory
by integrating non-holonomic motion constraints and physical constraints into
the generative prediction module. Central to the architecture of cVMD is its
capacity to perform uncertainty quantification, a feature that is crucial in
safety-critical applications. By integrating the quantified uncertainty into
the prediction process, the cVMD's trajectory prediction performance is
improved considerably. The model's performance was evaluated using the publicly
available highD dataset. Experiments show that the proposed architecture
achieves competitive trajectory prediction accuracy compared to
state-of-the-art models, while providing guaranteed drivable trajectories and
uncertainty quantification.

通过集成驱动能力的约束和物理约束条件的生成预测模块，本研究提出了一种基于扩散模型的高速公路轨迹预测网络架构，通过整合量化的不确定性进行预测，以提高模型的性能。该模型在公开可用的高 D 数据集上进行了评估实验，结果表明在提供可行驶轨迹和不确定性量化的同时，与最先进的模型相比具有竞争性的轨迹预测准确性。