用历史运动先验提升自动驾驶视频预测的连贯性与质量
HMPDM: A Diffusion Model for Driving Video Prediction with Historical Motion Priors
- 引入历史运动先验，通过时序感知条件注入增强运动理解
- 在Cityscapes上实现FVD降低28.2%，显著提升生成连贯性
- 适合自动驾驶场景下的视频预测研究者与工程团队参考
视频预测对自动驾驶至关重要，可帮助车辆预判场景演变，支持安全决策。现有模型受限于多阶段训练流程，且难以捕捉真实驾驶场景中的多样化运动模式，导致时间一致性与视觉质量下降。本文提出历史运动先验引导的扩散模型（HMPDM），通过三个关键设计提升性能：(i) 时序感知潜在条件模块（TaLC）实现隐式历史运动注入；(ii) 运动感知金字塔编码器（MaPE）进行多尺度运动表征；(iii) 自条件策略保障稳定迭代去噪。在Cityscapes和KITTI基准上的大量实验表明，HMPDM在相同单目RGB输入条件下优于当前最优方法，城市景观数据集上FVD降低28.2%。代码已开源。
原文摘要 · Abstract (English)
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 introduces three key designs: (i) a Temporal-aware Latent Conditioning (TaLC) module for implicit historical motion injection; (ii) a Motion-aware Pyramid Encoder (MaPE) for multi-scale motion representation; (iii) a Self-Conditioning (SC) strategy for stable iterative denoising. Extensive experiments on the Cityscapes and KITTI benchmarks demonstrate that HMPDM outperforms state-of-the-art video prediction methods with efficiency, achieving a 28.2% improvement in FVD on Cityscapes under the same monocular RGB input configuration setting. The implementation codes are publicly available at https://github.com/KELISBU/HMPDM.
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