Distributed Model Predictive Control for Multi-Vehicle Autonomous Driving

Abstract

In this thesis, we consider the problem of controlling multiple autonomous vehicles in a highway scenario, via MPC. By iteratively solving a motion planning OCP, MPC is perfectly suited for unknown dynamic environments, while optimally computing path and vehicle inputs. Moreover, MPC can ensure the satisfaction of collision avoidance constraints, a prerequisite for safe automated driving.

The collision avoidance constraints render the OCP non-convex. This thesis tackles this non-convexity by either designing nonlinear MPC controllers, or by convexifying these non-convex constraints.

Moreover, control of a large, networked system of automated vehicles is achieved by designing local, subsystem-based controllers. We analyse three different algorithms to distribute the plantwide OCP. All controllers are subjected to an objective analysis and compared to see which is the most efficient and most practical to implement. Centralized MPC is used as benchmark, since this gives the plantwide optimal solution. The first decomposed algorithm is decentralized MPC, where subsystems communicate a single time every MPC iteration and compute their new trajectory based on the previously communicated trajectory of neighboring subsystems. The second method is based on sub-optimal cooperative distributed MPC. Here, vehicles perform multiple sub-optimal iterations of a Gauss-Jacobi type distributed optimization. For the last method, based on a Generalized Potential Game, the vehicles sequentially solve and communicate the solution of their local OCP in order to find an $\epsilon$-Nash Equilibrium. By relying on additional constraints or fixed ordering among vehicles, all three controllers are able to recursively feasible compute their own trajectory while avoiding other vehicles.

The distributed controllers are assessed in two different scenarios, using three different criteria, i.e., the overall effectiveness of the controller, the local effectiveness of the controller and the progress made, by each vehicle in the simulation. The first criteria gives an indication of the level of cooperation among vehicles, the second shows the individual satisfaction of each vehicle with respect to its reference, and the last represents the overall progress each vehicle has made in the highway simulation.