This paper proposes a novel non-linear lane selection algorithm for autonomous vehicles based on the Optimality Condition Decomposition algorithm. An initial algorithm based on mixed integer quadratic programming was studied, which proved untreatable due to its computational complexity, leading to similar formulations that do not require such a nature in their variables. As a result, a cascade strategy combining both distributed local planning and decision-making layers is implemented, leading to promising results. The proposed approach can determine paths that consider individual goals and the environment so that agents can collaborate to establish a formation that re-configures online, allowing vehicles to reach their individual goals.

In this paper, a novel distributed set-based localisation algorithm for autonomous vehicles based on zono-topes is proposed. The proposed approach structurally allows embedding data fusion locally and in a vehicle-to-vehicle or infrastructure-to-vehicle manner under treatable computational times. Moreover, the localisation is only based on zonotopic intersections, allowing each individual agent to freely acquire their local estimation by any means, being the single requirement the access of a piece of hardware providing a coupled measurement. The proposed approach is implemented in a distributed manner in a ROS network, mimicking the physical layout of a platoon of 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.