A One-Step Feasible Negotiation Algorithm for Distributed Trajectory Generation of Autonomous Vehicles

We propose a distributed trajectory generation method for connected autonomous vehicles. It is integrated in an intersection crossing scenario where we assume a given vehicle order provided by a high-level scheduling unit. The multi-vehicle framework is modeled by local independent vehicle dynamics with coupling constraints between neighboring vehicles. Each vehicle in the framework computes in parallel a local model predictive control (MPC) decision, which is shared with its neighbors after conducting a convex Jacobi update step. The procedure can be iteratively repeated within a sampling time-step to improve the overall coordination decisions of the multi-vehicle setup. However, iterations can be stopped after each inter-sampling step with a guaranteed feasible solution which satisfies local and coupling constraints. We construct feasible initial trajectory candidates and propose a method to emulate the centralized solution. This makes the Jacobi algorithm suitable for distributed trajectory generation of autonomous vehicles in low and medium speed driving. Simulation results compare the performance of the distributed Jacobi MPC scheme with the centralized solution and illustrate the feasibility guarantee in an intersection scenario with unforeseen events.