A Case Study on Optimization of Platooning Coordination

TL;DR

This paper presents a framework for optimizing platooning coordination strategies in self-adaptive systems, enhancing traffic efficiency and safety by dynamically selecting and tuning strategies based on current conditions.

Contribution

It introduces a novel framework for self-aware optimization of platooning strategies, integrating situation detection, strategy selection, and parameter tuning.

Findings

Improved platooning coordination performance in case study

Effective adaptation to changing traffic conditions

Demonstrated benefits of dynamic strategy optimization

Abstract

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.