Pedestrian trajectory prediction is essential for collision avoidance in
autonomous driving and robot navigation. However, predicting a pedestrian's
trajectory in crowded environments is non-trivial as it is influenced by other
pedestrians' motion and static structures that are present in the scene. Such
human-human and human-space interactions lead to non-linearities in the
trajectories. In this paper, we present a new spatio-temporal graph based Long
Short-Term Memory (LSTM) network for predicting pedestrian trajectory in
crowded environments, which takes into account the interaction with static
(physical objects) and dynamic (other pedestrians) elements in the scene. Our
results are based on two widely-used datasets to demonstrate that the proposed
method outperforms the state-of-the-art approaches in human trajectory
prediction. In particular, our method leads to a reduction in Average
Displacement Error (ADE) and Final Displacement Error (FDE) of up to 55% and
61% respectively over state-of-the-art approaches.

本文提出了一种基于时空图的长短期记忆神经网络，用于在拥挤环境中预测行人的轨迹，考虑与场景中的静态和动态元素的交互，结果表明，相比于其他方法，本方法在人类轨迹预测方面能够将平均位移误差（ADE）和最终位移误差（FDE）分别降低高达 55％和 61％。

基于时空关注模型的情境感知行人轨迹预测

Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal  Attention Model

Mobility in an effective and socially-compliant manner is an essential yet
challenging task for robots operating in crowded spaces. Recent works have
shown the power of deep reinforcement learning techniques to learn socially
cooperative policies. However, their cooperation ability deteriorates as the
crowd grows since they typically relax the problem as a one-way Human-Robot
interaction problem. In this work, we want to go beyond first-order Human-Robot
interaction and more explicitly model Crowd-Robot Interaction (CRI). We propose
to (i) rethink pairwise interactions with a self-attention mechanism, and (ii)
jointly model Human-Robot as well as Human-Human interactions in the deep
reinforcement learning framework. Our model captures the Human-Human
interactions occurring in dense crowds that indirectly affects the robot's
anticipation capability. Our proposed attentive pooling mechanism learns the
collective importance of neighboring humans with respect to their future
states. Various experiments demonstrate that our model can anticipate human
dynamics and navigate in crowds with time efficiency, outperforming
state-of-the-art methods.

本文提出一种注意力机制的深度强化学习模型，能够更好地模拟拥挤场景下的人机交互问题，包括人与人的交互作用，实验结果表明该模型优于现有方法。