Predicting the future motion of actors in a traffic scene is a crucial part of any autonomous driving system. Recent research in this area has focused on trajectory prediction approaches that optimize standard trajectory error metrics. In this work, we describe three important properties -- physical realism guarantees, system maintainability, and sample efficiency -- which we believe are equally important for developing a self-driving system that can operate safely and practically in the real world. Furthermore, we introduce PTNet (PathTrackingNet), a novel approach for vehicle trajectory prediction that is a hybrid of the classical pure pursuit path tracking algorithm and modern graph-based neural networks. By combining a structured robotics technique with a flexible learning approach, we are able to produce a system that not only achieves the same level of performance as other state-of-the-art methods on traditional trajectory error metrics, but also provides strong guarantees about the physical realism of the predicted trajectories while requiring half the amount of data. We believe focusing on this new class of hybrid approaches is an useful direction for developing and maintaining a safety-critical autonomous driving system.

中文翻译：

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物理上可行的车辆轨迹预测

预测交通场景中演员的未来运动是任何自动驾驶系统的关键部分。该领域的最新研究集中在优化标准轨迹误差度量的轨迹预测方法上。在这项工作中，我们描述了三个重要属性-物理现实性保证，系统可维护性和采样效率-我们认为这对于开发可以在现实世界中安全实用地运行的自动驾驶系统同样重要。此外，我们介绍了PTNet（PathTrackingNet），这是一种用于车辆轨迹预测的新方法，它是经典的纯追踪路径跟踪算法和现代基于图的神经网络的混合体。通过将结构化的机器人技术与灵活的学习方法相结合，我们能够生产出一种系统，该系统不仅可以在传统轨迹误差度量上达到与其他最新方法相同的性能水平，而且还可以为预测轨迹的物理真实性提供有力的保证，而所需的数量仅为一半数据的。我们认为，专注于此类新型混合动力方法是开发和维护对安全至关重要的自动驾驶系统的有用方向。