Abstract
Describing an accurate simulation model of the driving behavior of real-world vehicles is a laborious or even impossible task, because a driver reacts to a dynamically changing environment. As multiple external factors determine driving behavior, it is usually difficult to obtain an accurate model, owing to a lack of sensors or inability to collect data. In this paper, we propose a novel technique to combine driving behavior in vehicular Internet of Things (IoT) big data with a multi-agent system. This enables correct and scalable simulation without modeling the behavior of vehicular IoT devices or the environment. We develop an extensible simulation framework, called FlowSim, that demonstrates the application of our technique for a simulation of camera-image data collection from connected cars.
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Notes
- 1.
It is desirable that one real vehicle is assigned to only one virtual vehicle, from the viewpoint of correctness; otherwise, this can duplicate the behavior of the real vehicle in the simulation, which is unrealistic. To this end, a sufficient number of real vehicles are required, namely \(|U| -|O| \ll |O|\) when \(|U| > |O|\).
- 2.
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Neyama, R. et al. (2020). Simulating Vehicular IoT Applications by Combining a Multi-agent System and Big Data. In: Baroglio, C., Hubner, J.F., Winikoff, M. (eds) Engineering Multi-Agent Systems. EMAS 2020. Lecture Notes in Computer Science(), vol 12589. Springer, Cham. https://doi.org/10.1007/978-3-030-66534-0_8
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