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We propose an intersection crossing algorithm for autonomous vehicles with vehicle to infrastructure (V2I) communication capability. All vehicles attempting to cross the intersection share their expected time of entering a critical zone based on decentralized model predictive control (MPC) results. These time suggestions are collected at a central intersection management (IM) unit, which is responsible for coordinating the vehicles. A time-based negotiation process between vehicles and IM is conducted to find a safe solution. An advantage of the approach is that model-based vehicle data is kept private, while the computational burden of the intersection coordination is distributed between the central IM and the vehicles. We prove the existence of a feasible solution and illustrate the introduced negotiation algorithm by simulation of an intersection crossing scenario with disturbances. The results show that vehicles remain in a safe distance without sharing private data.

We introduce a distributed control method for coordinating multiple vehicles in the framework of an automated valet parking (AVP) system. The control functionality is distributed between an infrastructure server, called parking area management (PAM) system, and local autonomous vehicle control units. Via a vehicle-to-infrastructure (V2I) communication interface, model predictive control (MPC) decisions of the vehicles are shared with the coordination unit in the PAM. This unit in turn computes a coupling feedback which is shared with the vehicles. The control system is integrated in an automated test-system to cope with the high test requirements and short development cycles of highly automated systems. Evaluations conducted with the test-system show the functionality of the proposed distributed control method for multi-vehicle coordination. Results indicate safe coordination, and an efficiency increase compared to an uncoordinated method in an AVP simulation environment.

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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.