Bezerra Viana, IcaroKanchwala, HusainAhiska, KenanAouf, Nabil2021-01-262021-01-262021-01-07Viana IB, Kanchwala H, Ahiska K, Aouf N. (2021) A comparison of trajectory planning and control frameworks for cooperative autonomous driving. Journal of Dynamic Systems, Measurement, and Control, Volume 143, Issue 7, July 2021, Article number DS-20-13180022-0434https://doi.org/10.1115/1.4049554https://asmedigitalcollection.asme.org/dynamicsystems/article/doi/10.1115/1.4049554/1094099/A-Comparison-of-Trajectory-Planning-and-Controlhttp://dspace.lib.cranfield.ac.uk/handle/1826/16258This work considers the cooperative trajectory-planning problem along a double lane change scenario for autonomous driving. In this paper we develop two frameworks to solve this problem based on distributed model predictive control (MPC). The first approach solves a single non-linear MPC problem. The general idea is to introduce a collision cost function in the optimization problem at the planning task to achieve a smooth and bounded collision function and thus to prevent the need to implement tight hard constraints. The second method uses a hierarchical scheme with two main units: a trajectory-planning layer based on mixed-integer quadratic program (MIQP) computes an on-line collision-free trajectory using simplified motion dynamics, and a tracking controller unit to follow the trajectory from the higher level using the non-linear vehicle model. Connected and automated vehicles (CAVs) sharing their planned trajectories lay the foundation of the cooperative behaviour. In the tests and evaluation of the proposed methodologies, MATLAB-CARSIM co-simulation is utilized. CARSIM provides the high fidelity model for the multi-body vehicle dynamics. MATLAB-CARSIM conjoint simulation experiments compare both approaches for a cooperative double lane change maneuver of two vehicles moving along a one-way three-lane road with obstacles.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Article