Accurate prediction of others' trajectories is essential for autonomous
driving. Trajectory prediction is challenging because it requires reasoning
about agents' past movements, social interactions among varying numbers and
kinds of agents, constraints from the scene context, and the stochasticity of
human behavior. Our approach models these interactions and constraints jointly
within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the
model encodes multiple agents' past trajectories and the scene context into a
Multi-Agent Tensor, then applies convolutional fusion to capture multiagent
interactions while retaining the spatial structure of agents and the scene
context. The model decodes recurrently to multiple agents' future trajectories,
using adversarial loss to learn stochastic predictions. Experiments on both
highway driving and pedestrian crowd datasets show that the model achieves
state-of-the-art prediction accuracy.

自动驾驶中准确预测其他车辆 / 行人的运动轨迹非常重要，本文提出了一种基于多智能体张量融合网络的模型，可以模拟代理人之间的交互和约束以及环境场景等因素，从而实现对未来运动轨迹的精准预测，并在高速公路和行人数据集上表现出最先进的预测精度。