Abstract
Artificial vision is a key factor for new generation automotive systems. This paper focuses on a module aimed at maximizing the energy flow between the transmitting and receiving grids, in the context of dynamic wireless charging of electrical vehicles. The output of the module helps the driver to keep a precise alignment between the vehicle and the charging grids in the road. The module was developed using low cost and open hardware and software components. This paper provides a characterization of the embedded system from a performance point of view, considering various parameters, such as CPU load, memory footprint, and energy consumption, in view of assessing the Raspberry Pi as a platform for embedded rapid prototyping and computing in automotive environment.
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Acknowledgements
We would like to thank the FABRIC coordinator, Prof. Angelos Amditis and all the colleagues that allowed a successful performance of the project.
This work was supported in part by the EU, under the Feasibility Analysis and Development of on-road charging solutions for future electric vehicles (FABRIC) integrated project (FP7-SST-2013-RTD-1 605405).
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Kobeissi, A., Bellotti, F., Berta, R., De Gloria, A. (2019). Raspberry Pi 3 Performance Characterization in an Artificial Vision Automotive Application. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2018. Lecture Notes in Electrical Engineering, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-030-11973-7_1
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DOI: https://doi.org/10.1007/978-3-030-11973-7_1
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