We propose to predict the future trajectories of observed agents (e.g.,
pedestrians or vehicles) by estimating and using their goals at multiple time
scales. We argue that the goal of a moving agent may change over time, and
modeling goals continuously provides more accurate and detailed information for
future trajectory estimation. To this end, we present a recurrent network for
trajectory prediction, called Stepwise Goal-Driven Network (SGNet). Unlike
prior work that models only a single, long-term goal, SGNet estimates and uses
goals at multiple temporal scales. In particular, it incorporates an encoder
that captures historical information, a stepwise goal estimator that predicts
successive goals into the future, and a decoder that predicts future
trajectory. We evaluate our model on three first-person traffic datasets
(HEV-I, JAAD, and PIE) as well as on three bird's eye view datasets (NuScenes,
ETH, and UCY), and show that our model achieves state-of-the-art results on all
datasets. Code has been made available at:
this https URL

本文提出了 Stepwise Goal-Driven Network (SGNet) 的循环网络来预测观察到的行人或车辆在多个时间尺度上的轨迹，并通过连续建模目标的变化提供更准确和详细的信息。作者在三个第一人称交通数据集和三个鸟瞰数据集上对模型进行了评估，并展示了在所有数据集上都取得了最先进的结果。

基于目标驱动的阶段性网络用于轨迹预测

Stepwise Goal-Driven Networks for Trajectory Prediction

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。