A Multi-Vehicle Control Framework With Application to Automated Valet Parking

Abstract

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.

Transport Automation in Urban Mobility: A Case Study of an Autonomous Parking System

“…A new privacy-preserving reservation scheme for securing APS was developed in [25], which is based on the following policy: each anonymous user may only have one valid reservation token at any given moment, and the token can only be used to book one vacant parking space once. Furthermore, communication via vehicle-to-infrastructure and model predictive control functionality distributed among multiple vehicles and infrastructures was studied in [26]. In this context, cooperative automated valet parking was examined in [27], where the authors proposed a solution involving multi-agent pathfinding.…”

A Middleware Protocol for Time-Critical Wireless Communication of Large Data Samples

“…We start the evaluation of W2RP by defining a default parameterization shown in Table II. Considering the automated 280.9 µs, 34.4 µs valet parking use case from section I, we set P S = D S to 100 ms, which is a common sampling rate in such setups [9].…”

“…The related age requirement is defined as sample deadline (D S ) that determines the permitted sample latency. In our use case, we define D S = P S , whereby P S is 100 ms [9]. The sample size (S S ) is determined by camera resolution and fusion requirement and typically ranges between tens of kB to several MB.…”

Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field

“…Subsequently, the trajectory prediction of the human driver model was integrated into the framework, such that the behaviors of the other agents were affected by the human-operated vehicles (HVs) [45]. In [46], a distributed control method for coordinating multiple vehicles in the framework of an automated valet parking system was introduced. The main limitation of this approach is to rely on traffic infrastructure, which poses a considerable challenge to the current traffic facilities.…”