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.

Abstract-In practice often multiple control applications share a communication channel, requiring a smart and scalable scheduling mechanism to coordinate the access to the capacitylimited communication medium. In this paper, we propose a decentralized event-triggered medium access control (MAC) for multiple feedback control loops which are coupled through a capacity-limited communication medium. The individual control loops are assumed to be linear time-invariant (LTI) with stochastic heterogeneous plants. Noisy state measurements from local sensors are transmitted through a shared communication medium to their respective control units. Due to capacity limitations in the shared communication channel, not all sensors are allowed to transmit simultaneously. To allocate the scarce resources, a decentralized MAC which prioritizes the channel access according to a real-time error-dependent measure, is introduced. The prioritization is orchestrated via a combined deterministic and probabilistic mechanism aiming at the efficient allocation of the limited capacity. We study stability of the described multi-loop NCS under the proposed MAC design in terms of Lyapunov stability in probability (LSP). It is demonstrated that the collision rate remains low by properly tuning the MAC parameters. Numerical results show that the proposed MAC design significantly outperforms conventional time-triggered and random access schemes, while its performance closely follows the centralized TOD approach.

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