研究人员开发了一个名为MMPM的新框架，以改进从第一人称视角视频进行的行人轨迹预测。该模型通过单独模拟不同的模式（例如过马路或不过马路）来应对多模态行人行为的挑战。MMPM框架包括一个行为感知的行人交互模块（PIM）和一个基于CVAE的模式感知轨迹预测器（MTP），它们共同捕捉复杂的交互和意图。在PIE和JAAD数据集上的实验表明，MMPM的性能优于现有的最先进方法，并且可以与BiTrap-NP和SGNet-ED等其他框架集成。 AI
影响 提高了在复杂城市环境中预测行人运动的准确性，可能改进自主导航和安全系统。
排序理由 该集群包含一篇详细介绍行人轨迹预测新框架的研究论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian. By modelling correlation and intent f…
Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian. By modelling correlation and intent from the historical and future trajectories of the …