As self-driving vehicles progress toward real-world deployment, efficient and reliable motion planning in dynamic multi-agent environments becomes increasingly essential. This work addresses this challenge by advancing the field of nonlinear distributed model predictive control (NMPC) for autonomous multi-vehicle coordination. The approach focuses on complex, interaction-dense scenarios where space is limited. To model vehicle shapes and collision boundaries efficiently it uses polytopic set representations to approximate vehicle geometries and collision boundaries instead of conventional shapes such as ellipsoids.

We propose a novel distributed NMPC approach for navigation in tight environments. The goal is to enable coordinated motion planning for multiple autonomous vehicles in dense traffic scenarios. This can be easily modelled with centralised formulations, however, considering the scale of the road network they become computationally intractable as the number of agents grows. In distributed approaches instead each vehicle solves its local part of the problem which scales linearly and is therefore better suited for these kind of environments. So building on an existing distributed method we enhance its structure to improve scalability, safety, and performance in cooperative autonomous driving tasks. The research is guided by three objectives: (RO1) to reproduce a known distributed baseline algorithm using the DART (Delft’s Autonomous-driving Robotic Testbed) vehicle model \cite{lyons_dart_2024}, the Model Predictive Contouring Control (MPCC) \cite{lam_model_2010} tracking objective, and the ACADOS solver framework \cite{verschueren_acados_2020}; (RO2) to develop a novel distributed MPC-based algorithm that improves inter-vehicle spacing and tracking performance while generalizing beyond predefined reference trajectories; and (RO3) to prepare the algorithm for validation on a real-world robotic testbed.

The primary contributions of this work include: (C1) successful reproduction of the distributed baseline using open-source tools and realistic vehicle modelling; (C2) development of two enhanced distributed algorithms, Distributed Model Predictive Contouring Control with Relaxed Collision Avoidance (DMPC-RCA) and DMPC-RCA with consensus (DMPC-RCA-C), that demonstrate superior performance in tracking accuracy and safety margins; and (C3) integration of these algorithms with the DART hardware platform for future experimental validation. The repository of these approaches can be found in https://github.com/HubertVisser/multi-vehicle-coordination-algorithm.git

Performance is evaluated in two representative scenarios, a merging situation and a T-junction, with the centralised NMPC approach serving as the performance benchmark. To evaluate the performance of the proposed designs, three key metrics are used: accumulated tracking cost, computation time, and minimum inter-agent distance. The results show that both proposed methods achieve tracking costs comparable to the centralised controller, while significantly outperforming the distributed baseline method. Notably, the inclusion of a consensus term yields no substantial improvement in performance over the non-consensus version.

To conclude, the proposed approaches offer strong potential for scalable, safe, and efficient multi-agent motion planning, moving one step closer to the deployment of fully cooperative autonomous driving on public roads.

Github repository for the Master Thesis - https://github.com/HubertVisser/multi-vehicle-coordination-algorithm.git