GoalNet：面向目标区域的行人轨迹预测
GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction
AI总结：
提出GoalNet神经网络，利用场景上下文和观测轨迹先预测行人目标点再生成未来轨迹，在JAAD和PIE数据集上分别将最先进性能提升48.7%和40.8%。
摘要：
预测道路上行人的未来轨迹是自动驾驶的一项重要任务。行人轨迹预测受场景路径、行人意图和决策的影响，这是一个多模态问题。最近的大多数研究使用过去的轨迹来预测各种潜在的未来轨迹分布，这些研究没有考虑场景上下文和行人目标。我们不直接预测未来轨迹，而是提出使用场景上下文和观测到的轨迹首先预测目标点，然后重用这些目标点来预测未来轨迹。通过利用来自场景上下文和观测轨迹的信息，可以将不确定性限制在少数目标区域内，这些区域代表了行人的“目标”。在本文中，我们提出了GoalNet，一种基于行人目标区域的新型轨迹预测神经网络。我们的网络可以同时预测行人的轨迹和边界框。整体模型高效且模块化，其输出可以根据使用场景进行更改。实验结果表明，GoalNet在JAAD数据集上将先前的最先进性能显著提高了48.7%，在PIE数据集上提高了40.8%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
