Pedestrian trajectory forecasting is a fundamental task in multiple utility areas, such as self-driving, autonomous robots, and surveillance systems. The future trajectory forecasting is multi-modal, influenced by physical interaction with scene contexts and intricate social interactions among pedestrians. The mainly existing literature learns representations of social interactions by deep learning networks, while the explicit interaction patterns are not utilized. Different interaction patterns, such as following or collision avoiding, will generate different trends of next movement, thus, the awareness of social interaction patterns is important for trajectory forecasting. Moreover, the social interaction patterns are privacy concerned or lack of labels. To jointly address the above issues, we present a social-dual conditional variational auto-encoder (Social-DualCVAE) for multi-modal trajectory forecasting, which is based on a generative model conditioned not only on the past trajectories but also the unsupervised classification of interaction patterns. After generating the category distribution of the unlabeled social interaction patterns, DualCVAE, conditioned on the past trajectories and social interaction pattern, is proposed for multi-modal trajectory prediction by latent variables estimating. A variational bound is derived as the minimization objective during training. The proposed model is evaluated on widely used trajectory benchmarks and outperforms the prior state-of-the-art methods.
翻译:Pedestrian 轨迹预测是多用途领域的基本任务,如自驾驶、自主机器人和监视系统。未来的轨迹预测是多模式的,受与现场环境物理互动和行人之间复杂的社会互动的影响。主要现有文献通过深层次学习网络了解社会互动的表现形式,而没有使用明确的互动模式。不同的互动模式,如跟踪或避免碰撞,将产生下一轮运动的不同趋势,因此,社会互动模式的认识对于轨迹预测很重要。此外,社会互动模式涉及隐私或缺乏标签。为了共同解决上述问题,我们为多模式轨迹预测提出一种社会有条件的、有条件的自动变异变式(社会-二元CVAE)自动变式(社会-二元CVAE ), 其基础是一个不仅基于过去轨迹的基因化模型,而且还包括未经监督的对互动模式的分类。在生成未标记的社会互动模式的类别分布后, 以过去的轨迹和社会互动模式和标签模式模式模式模式模式的缺失为条件。我们提议用潜在轨迹模型来进行多模式的模拟轨迹预测。在使用之前评估时,对模型进行最起码的轨迹图进行了评估。