Human-Like Trajectory Planning on Curved Road: Learning From Human Drivers

Abstract

The ultimate goal of self-driving technologies is to offer a safe and human-like driving experience. As one of the most important enabling functionalities, trajectory planning has been extensively studied from the perspective of safety. However, human-like trajectory planning on curved roads has rarely been studied. In this paper, we characterize and model human driving using extensive experimental driving collected on an urban curved road with 30 participants (10 experienced and 20 novice drivers) and five vehicles of different types. Differential global positioning system (GPS) is used to measure vehicle positions in high precision. We study factors that affect the driving trajectory, including vehicle speed, road curvature, and sight distance. We find that the human drivers typically do not follow lane centerline and the human-driven trajectories are very different from planners like rapidly exploring random tree (RRT). To generate human-like driving trajectory, we develop a data-driven trajectory model using general regression neural network (GRNN). The model was validated in various cases with promising performance.