Predicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths, pedestrian's intentions and decision-making, which is a multi-modal problem. Most recent studies use past trajectories to predict a variety of potential future trajectory distributions, which do not account for the scene context and pedestrian targets. Instead of predicting the future trajectory directly, we propose to use scene context and observed trajectory to predict the goal points first, and then reuse the goal points to predict the future trajectories. By leveraging the information from scene context and observed trajectory, the uncertainty can be limited to a few target areas, which represent the "goals" of the pedestrians. In this paper, we propose GoalNet, a new trajectory prediction neural network based on the goal areas of a pedestrian. Our network can predict both pedestrian's trajectories and bounding boxes. The overall model is efficient and modular, and its outputs can be changed according to the usage scenario. Experimental results show that GoalNet significantly improves the previous state-of-the-art performance by 48.7% on the JAAD and 40.8% on the PIE dataset.

翻译：预测道路上行人未来轨迹是自动驾驶的重要任务。行人轨迹预测受场景路径、行人意图及决策影响，属于多模态问题。大部分近期研究利用历史轨迹预测多种潜在未来轨迹分布，但未考虑场景上下文及行人目标。本文不直接预测未来轨迹，而是首先利用场景上下文与观测轨迹预测目标点，再基于目标点预测未来轨迹。通过整合场景上下文与观测轨迹信息，可将不确定性限定在少数代表行人"目标"的区域中。我们提出GoalNet——一种基于行人目标区域的新型轨迹预测神经网络。该网络可同时预测行人轨迹与边界框。整体模型高效且模块化，输出结果可根据使用场景调整。实验结果表明，在JAAD和PIE数据集上，GoalNet相较此前最优方法分别显著提升48.7%和40.8%的性能。