HMPDM: A Diffusion Model for Driving Video Prediction with Historical Motion Priors
Video prediction is a useful function for autonomous driving, enabling intelligent vehicles to reliably anticipate how driving scenes will evolve and thereby supporting reasoning and safer planning. However, existing models are constrained by multi-stage training pipelines and remain insufficient in modeling the diverse motion patterns in real driving scenes, leading to degraded temporal consistency and visual quality. To address these challenges, this paper introduces the historical motion priors-informed diffusion model (HMPDM), a video prediction model that leverages historical motion priors to enhance motion understanding and temporal coherence. The proposed deep learning system intro...
| Authors | Ke Li, Tianjia Yang, Kaidi Liang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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