A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach

TLDR

Motion planning is essential for autonomous driving, enabling vehicles to generate safe, comfortable, economical, and human‑like movement sequences across space and time. The study proposes a new motion planning and tracking framework for autonomous vehicles that leverages an artificial potential field elaborated resistance network approach. The framework uses an artificial potential field to assign potentials to obstacles and boundaries, meshes the drivable area with resistance values, and employs a local current comparison method to generate collision‑free paths; planning is split into virtual and actual spaces, with the virtual space predicting short‑horizon trajectories and the actual space tracking them via a Carsim model controlled by a model‑predictive controller. Case studies confirm the effectiveness of the proposed framework.

Abstract

This paper presents a novel motion planning and tracking framework for automated vehicles based on artificial potential field (APF) elaborated resistance approach. Motion planning is one of the key parts of autonomous driving, which plans a sequence of movement states to help vehicles drive safely, comfortably, economically, human-like, etc. In this paper, the APF method is used to assign different potential functions to different obstacles and road boundaries; while the drivable area is meshed and assigned resistance values in each edge based on the potential functions. A local current comparison method is employed to find a collision-free path. As opposed to a path, the vehicle motion or trajectory should be planned spatiotemporally. Therefore, the entire planning process is divided into two spaces, namely the virtual and actual. In the virtual space, the vehicle trajectory is predicted and executed step by step over a short horizon with the current vehicle speed. Then, the predicted trajectory is evaluated to decide if the speed should be kept or changed. Finally, it will be sent to the actual space, where an experimentally validated Carsim model controlled by a model predictive controller is used to track the planned trajectory. Several case studies are presented to demonstrate the effectiveness of the proposed framework.