Roadside Sensor Optimization in Infrastructure-based Teleoperation of Vehicles

Konferenz: AmE 2019 – Automotive meets Electronics - 10. GMM-Fachtagung
12.03.2019 - 13.03.2019 in Dortmund, Deutschland

Tagungsband: GMM-Fb. 93: AmE 2019

Seiten: 6Sprache: EnglischTyp: PDF

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Tarkanyi, Ildiko; Icking, Christian; Ma, Lihong (FernUniversität in Hagen, Germany)
Gay, Nicolas; Hili, Graham; Dannheim, Clemens (Luxoft, Munich, Germany)
Neumeier, Stefan (Technische Hochschule Ingolstadt, Germany)

Infrastructure-based teleoperation relies on roadside sensors and offers an alternative approach for data transmission in teleoperation of remote vehicles. Since the number of deployed sensors within a field of interest is often limited by physical or economical constraints, their placement is of fundamental importance. This paper describes a self-learning solution for the sensor placement problem based on Fritzke’s Growing Neural Gas algorithm. The proposed method iteratively computes the sensor locations, trying to reach a given road coverage threshold.