System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

Abstract

While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming longterm transition period - the hybrid vehicular traffic - is often neglected. However, within the next decades, automotive systems with heterogeneous autonomy levels will share the same road network, resulting in new problems for traffic management systems and communication network infrastructure providers. In this paper, we identify key challenges of the upcoming hybrid traffic scenario and present a system-of-systems model, which brings together approaches and methods from traffic modeling, data science, and communication engineering in order to allow data-driven traffic flow optimization. The proposed model consists of data acquisition, data transfer, data analysis, and data exploitation and exploits real world sensor data as well as simulative optimization methods. Based on the results of multiple case studies, which focus on individual challenges (e.g., resource-efficient data transfer and dynamic routing of vehicles), we point out approaches for using the existing infrastructure with a higher grade of efficiency.

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• The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting
• Empirical Analysis of Client-based Network Quality Prediction in Vehicular Multi-MNO Networks
• Data-driven Network Simulation for Performance Analysis of Anticipatory Vehicular Communication Systems
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• Towards Data-driven Simulation of End-to-end Network Performance Indicators