Pedestrian trajectory prediction is an important technique of autonomous driving, which has become a research hot-spot in recent years. Previous methods mainly rely on the position relationship of pedestrians to model social interaction, which is obviously not enough to represent the complex cases in real situations. In addition, most of existing work usually introduce the scene interaction module as an independent branch and embed the social interaction features in the process of trajectory generation, rather than simultaneously carrying out the social interaction and scene interaction, which may undermine the rationality of trajectory prediction. In this paper, we propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN) which aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. In detail, when modeling social interaction, we propose a new \emph{social soft attention function}, which fully considers various interaction factors among pedestrians. And it can distinguish the influence of pedestrians around the agent based on different factors under various situations. For the physical interaction, we propose one new \emph{sequential scene sharing mechanism}. The influence of the scene on one agent at each moment can be shared with other neighbors through social soft attention, therefore the influence of the scene is expanded both in spatial and temporal dimension. With the help of these improvements, we successfully obtain socially and physically acceptable predicted trajectories. The experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.
翻译:Pedestrian轨迹预测是自主驾驶的重要技术,近年来已成为研究热点。以往的方法主要依靠行人的位置关系来模拟社会互动,这显然不足以代表真实情况下的复杂情况。此外,大多数现有工作通常将现场互动模块作为一个独立分支引入现场互动模块,并将社会互动特征嵌入轨迹生成过程中,而不是同时进行社会互动和场景互动,这可能会破坏轨迹预测的合理性。在本文中,我们提议了一个新的预测模型,名为社会软注意力图表集网(SSAGCN),目的是同时处理行人之间的社会互动以及行人与环境之间的场面互动。详细而言,在模拟社会互动时,我们建议采用新的场面互动模块,以充分考虑行人之间各种互动因素,而不是同时进行社会互动,这可能会破坏轨迹预测的合理性。关于物理互动,我们提出了一个新的SMET(Soft Tecontical Commission Commission) 。每个场面的场景影响在行人际行人际互动和场景互动中都得到了可接受的预测。我们通过软轨迹上的实验可以理解的社会层面,我们所看到的空间和感官能获取到的图像。