  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.
