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 nonlinear 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 nonlinear vehicle model. Connected and automated vehicles (CAVs) sharing their planned trajectories lay the foundation of the cooperative behavior. In the tests and evaluation of the proposed methodologies, matlab-carsim cosimulation is utilized. carsim provides the high-fidelity model for the multibody 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.

In this study, a survey on control allocation algorithms in tactical level control for path tracking unmanned surface vehicles (USV) is conducted. The strategic goal in the path tracking problem is to assist navigation solution of an unmanned underwater vehicle (UUV). USV with the help of its onboard acoustic sensors, tracks UUV according to constant bearing guidance rule. The survey on tactical controllers comprises comparisons of tracking performances of USVs under proportionalintegral-derivative, pole placement, feedback linearization and sliding mode controllers according to the strategic goal. The parameters of the controllers are tuned with a common elitist genetic algorithm optimization infrastructure. The disturbance rejection capabilities of the controllers are discussed through Monte Carlo simulations of USVs within various wave disturbances as well.

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