A comparison of trajectory planning and control frameworks for cooperative autonomous driving

Abstract

This 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.