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 based on distributed model predictive control (MPC). The first approach solves single nonlinear MPC problem. general idea is introduce collision cost function in optimization at planning task achieve smooth and bounded function, thus prevent need implement tight hard constraints. second method uses hierarchical scheme with main units: layer mixed-integer quadratic program (MIQP) computes an on-line collision-free trajectory using simplified motion dynamics, tracking controller unit follow from higher level vehicle model. Connected automated vehicles (CAVs) sharing their planned trajectories lay foundation of behavior. tests evaluation proposed methodologies, matlab-carsim cosimulation utilized. carsim provides high-fidelity multibody dynamics. conjoint simulation experiments compare both approaches maneuver moving one-way three-lane road obstacles.