In this paper, we present Goal-GAN, an interpretable and end-to-end trainable model for human trajectory prediction. Inspired by human navigation, we model the task of trajectory prediction as an intuitive two-stage process: (i) goal estimation, which predicts the most likely target positions of the agent, followed by a (ii) routing module which estimates a set of plausible trajectories that route towards the estimated goal. We leverage information about the past trajectory and visual context of the scene to estimate a multi-modal probability distribution over the possible goal positions, which is used to sample a potential goal during the inference. The routing is governed by a recurrent neural network that reacts to physical constraints in the nearby surroundings and generates feasible paths that route towards the sampled goal. Our extensive experimental evaluation shows that our method establishes a new state-of-the-art on several benchmarks while being able to generate a realistic and diverse set of trajectories that conform to physical constraints.

本研究提出了Goal-GAN，这是一个可解释性且可端到端训练的模型，用于人类轨迹预测，并通过将轨迹预测的任务建模为直观的两阶段过程：目标估计和路由模块，来实现该任务。我们使用了过去的轨迹信息和场景的视觉背景，来估计可能的目标位置的多模态概率分布，并使用其在推断过程中采样一个潜在的目标进行路由。我们使用一个循环神经网络来执行路由任务，该网络能够反应周围物理约束并生成符合这些约束的可行路径。我们的实验结果表明，我们的方法在多个基准测试中建立了一个新的最先进模型，并能够生成符合物理约束的逼真且多样化的轨迹集合。

Goal-GAN: 基于目标位置估计的多模态轨迹预测