A Case Study on Optimization of Platooning Coordination
This work addresses the need for adaptive software systems in dynamic environments like Intelligent Transportation Systems, but it is incremental as it builds on existing concepts in Self-Adaptive Systems.
The paper tackles the problem of self-aware optimization of adaptation planning strategies for Self-Adaptive Systems, specifically in platooning coordination, by designing a framework that includes situation detection, strategy selection, and parameter optimization, and evaluates its performance on a case study.
In today's world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one platooning coordination strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this paper, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.