Experimental Validation of a Behavior Tree-Based Lane Change Decision and Path Planning System for Autonomous Vehicle Racing

In this paper, we present a lane-change decision and trajectory generation system for autonomous vehicles operating on high-speed racing tracks, with a focus on system integration and experimental validation. The proposed approach is divided into three main modules. First, a driving mode determination module leverages a Behavior Tree to dynamically select the appropriate driving mode based on the autonomous driving system’s operational status, ensuring the system’s stability under various conditions. Second, a lane-change decision rule is designed by using a decision graph to evaluate drivable space represented as nodes, with spatiotemporal costs defining edges, thereby guaranteeing real-time feasibility and safety. Finally, the local path generation module produces a collision-free and comfortable trajectory to the target lane, taking into account traffic flow, vehicle dynamics, and road geometry. We validated the system’s robustness and efficiency through real-time experiments involving multiple vehicles on the Korea Intelligent Automobile Parts Promotion Institute (KIAPI) high-speed circuit, as well as virtual simulation environments which accurately replicates the real test track