Along-Edge Autonomous Driving on Curvy Roads Based on Frenet Frame: A Stable Hierarchical Planning Framework

Abstract

Along-edge driving, where an autonomous vehicle follows road edges, is increasingly common in urban environments and particularly challenging on curvy roads due to rapidly changing curvature. This paper presents a hierarchical trajectory planning framework that integrates Cartesian and Frenet frames to optimize along-edge motion. Cartesian planners struggle with nonlinear constraints, while Frenet-based approaches simplify edge-relative motion but often neglect trajectory curvature and suffer from non-convexity in obstacle avoidance. To address these limitations, our method employs an optimization-based planner with curvature constraints for precise along-edge motion and a sampling-based planner for stable lane changes when encountering obstacles. This novel approach maintains an along-edge distance within a precision of 0.1m, reducing error by 80% (from 0.7m to 0.1m). It also ensures smooth trajectory transitions and enhances stability in complex environments. Simulations and real-world experiments validate the framework’s efficiency, achieving an average planning time of 1.22ms per frame while effectively balancing accuracy, feasibility, and real-time performance.