Distributed Set-Based Planning in Autonomous Vehicles

Autonomous vehicles require sophisticated planning algorithms to navigate safely and efficiently in complex environments. Traditional centralised planning approaches face scalability challenges, especially as the number of agents increases. Distributed planning strategies have emerged as a promising solution to overcome these limitations. This paper presents a novel approach to distributed set-based planning for autonomous vehicles. Each vehicle can autonomously plan its path by lever-aging set-based representations of possible trajectories while considering uncertainty and dynamic environment changes. The proposed method enables vehicles to collaborate efficiently while maintaining decentralised decision-making capabilities, thus enhancing scalability and robustness. Furthermore, safe trajectories are derived by contemplating system uncertainties through set theory.