Reference Path Correction for Autonomous Ground Vehicles Driving Over Rough Terrain

This paper proposes an online reference path correction algorithm for Autonomous Ground Vehicles (AGVs) driving in unstructured environments. Because the reference path which is not in accord with the road geometry cannot be directly executed by AGV, we use a correction baseline, which represents the road geometry, as the guidance to correct reference path. As the road geometry varies according to the road environment, we use the centerline of drivable area extracted with General Regression Neural Network (GRNN) or optimal path generated by multi-resolution local trajectory planner as the correction baseline to correct the distance and curvature errors of the reference path. The proposed algorithm is tested on our experimental autonomous vehicle in the realistic off-road scenarios. Experimental results demonstrated its capability and efficiency to reduce the search space of the local trajectory planner and improve the robustness of tracking the reference path.