论文标题
目标gan：基于目标位置估计的多模式轨迹预测
Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation
论文作者
论文摘要
在本文中，我们提出了目标，这是一种可解释的端到端训练模型，用于人类轨迹预测。受到人类导航的启发，我们将轨迹预测的任务建模为一个直观的两阶段过程：（i）目标估计，该目标估算预测了代理的最可能目标位置，然后是（II）路由模块，该模块估计了一组沿估计目标路线的plausible轨迹。我们利用有关场景的过去轨迹和视觉上下文的信息来估计可能的目标位置多模式概率分布，该目标位置用于在推理过程中采样潜在的目标。该路由受复发性神经网络的控制，该神经网络对附近周围环境中的物理约束做出反应，并产生可行的路径，这些路径驶向采样目标。我们广泛的实验评估表明，我们的方法在几个基准上建立了一种新的最先进，同时能够产生符合物理约束的现实和多样化的轨迹。
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.
