Physically Feasible Vehicle Trajectory Prediction
This addresses the problem of ensuring safe and practical autonomous driving by focusing on physical feasibility, system maintainability, and sample efficiency, representing an incremental advance in hybrid methods.
The paper tackles vehicle trajectory prediction for autonomous driving by introducing PTNet, a hybrid approach combining pure pursuit path tracking with graph neural networks, achieving performance comparable to state-of-the-art methods on error metrics while providing physical realism guarantees and requiring half the data.
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.