Abstract
Recent advances in the Internet of Things (IoT) provides rich opportunities to future mobility services for the development of more flexible solutions. Instead of using a fixed set of data sources or services, applications can benefit from those flexible mechanisms by adapting to change in the sensing environment such as sensor disappearance/degradation or service unavailability. In this paper, we contribute with an approach that enables dynamic selection of the services for mobility to meet requirements of autonomous driving use-cases. Our approach is focused on different mobility services using available data sources and data processing methods with their related quality parameters. Those services are inspired by the standards and the dynamics of real-test road. We present a prototypical implementation of the mechanism for optimal service selection in an autonomous driving test environment and evaluated our testing results with respect to correctness and performance.
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Notes
- 1.
DigiNet-PS project website: http://diginet-ps.de.
- 2.
JIAC V, http://www.jiac.de/agent-frameworks/jiac-v/ [Accessed: May 30, 2019].
- 3.
CPLEX Optimizer: https://www.ibm.com/analytics/cplex-optimizer [Accessed: May 30, 2019].
- 4.
Apache Kafka: https://kafka.apache.org/ [Accessed: May 30, 2019].
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Eryilmaz, E., Khan, M.A., Trollmann, F., Albayrak, S. (2019). Adaptive Service Selection for Enabling the Mobility of Autonomous Vehicles. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds) Ambient Intelligence. AmI 2019. Lecture Notes in Computer Science(), vol 11912. Springer, Cham. https://doi.org/10.1007/978-3-030-34255-5_14
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