Threading the Needle—Overtaking Framework for Multi-agent Autonomous Racing

Multi-agent autonomous racing still remains a largely unsolved research challenge. The high-speed and close proximity situations that arise in multi-agent autonomous racing present an ideal condition to design algorithms which trade off aggressive overtaking maneuvers and minimize the risk of collision with the opponent. In this article we study a two-vehicle autonomous racing setup and present AutoPass—a novel framework for overtaking in a multi-agent setting. AutoPass uses the structure of an automaton to break down the complex task of overtaking into sub-maneuvers that balance overtaking likelihood and risk with safety of the ego vehicle. We present real-world implementation of 1/10-scale autonomous racing cars to demonstrate the effectiveness of AutoPass for the overtaking task. Our results indicate that the overtake success ratio for the AutoPass framework is 0.395, or 23 times more likely, compared to a purely reactive system at 0.017, while traditional Robot Operating System (ROS)-based path planners (depending on the navigation plugin used) are placed between 0.115 and 0.286.