Analyzing the Impact of Probabilistic Estimates on Communication Reliability at Intelligent Crossroads

Abstract

Intelligent crossroads aim to substitute conventional traffic lights by coordinating the order in which vehicles cross an intersection. Since vehicles come and go at arbitrary points in time, this results in an open-ended setting that is difficult to analyze with deterministic methods. In particular, deterministic methods fail to provide meaningful estimates of the maximum number of vehicles at the intersection, which is paramount to assess communication reliability and, in the end, guarantee safety. In contrast, statistical and probabilistic techniques are more suitable for this purpose and constitute the focus of this paper. We especially investigate how different driving directions and vehicle lengths influence the quality of probabilistic estimates in approximating the maximum number of vehicles at the intersection. These estimates are then incorporated into the design and analysis of the crossroad VANET to derive guarantees on communication reliability. Our results show that such estimates can greatly reduce pessimism and overdesign compared to deterministic approaches. These and other benefits are illustrated by means of a detailed case study and simulationsusing OMNeT++.