Raspberry Pi 3 Performance Characterization in an Artificial Vision Automotive Application
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.
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).
- 5.Cirimele, V., Smiai, O., Guglielmi, P., Bellotti, F., Berta, R., De Gloria, A.: Maximizing power transfer for dynamic wireless charging electric vehicles. In: International Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2016, Rome. Lecture Notes in Electrical Engineering, vol. 429, pp. 59–65 (2017). https://doi.org/10.1007/978-3-319-55071-8_8Google Scholar
- 6.Amditis, A. Karaseitanidis, G., Damousis, I., Guglielmi, P., Cirimele, V.: Dynamic wireless charging for more efficient FEVS: the fabric project concept, MedPower 2014, Athens, pp. 1–6 (2014)Google Scholar
- 7.Raspberry Pi 3 Model B. https://www.raspberrypi.org/products/raspberry-pi-3-model-b/
- 8.Marosi, A.C., Lovas, R., Kisari, Á., Simonyi, E.: A novel IoT platform for the era of connected cars. In: 2018 IEEE international conference on future IoT technologies (Future IoT), Eger, pp. 1–11 (2018)Google Scholar
- 11.Hajdarevic, K., Konjicija, S., Subasi, A.: A low energy APRS-IS client-server infrastructure implementation using Raspberry Pi. In: 2014 22nd Telecommunications Forum Telfor (TELFOR), Belgrade, pp. 296–299 (2014)Google Scholar
- 12.Cimino, D., Ferrero, A., Queirolo, L., Bellotti, F., Berta, R., De Gloria, A.: A low-cost, open-source cyber physical system for automated, remotely controlled precision agriculture, In: Proceedings of Applications in Electronics Pervading Industry, Environment and Society (APPLEPIES), Lecture Notes in Electrical Engineering, Rome, Sept. 215. Springer, ChamGoogle Scholar
- 13.Hassan, Q.F.: A Tutorial Introduction to IoT Design and Prototyping with Examples, in Internet of Things A to Z: Technologies and Applications, vol. 1, Wiley-IEEE Press (2018)Google Scholar
- 14.He, Q., Segee B., Weaver, V.: Raspberry Pi 2 B+ GPU Power, Performance, and Energy Implications. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, pp. 163–167 (2016)Google Scholar
- 15.Nunes, L.H., et al.: Performance and energy evaluation of RESTful web services in Raspberry Pi. In: 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC), Austin, TX, pp. 1–9 (2014)Google Scholar
- 16.Jupyter Notebook. https://jupyter.org/
- 17.Beyeler, M., OpenCV with Python Blueprints, Packt (2015)Google Scholar
- 18.Kobeissi, A., Bellotti, F., Berta, R., De Gloria, A.: IoT grid alignment assistant system for dynamic wireless charging of electric vehicles. In: 5th International Workshop on Intelligent Transportation and Connected Vehicles Technologies (ITCVT 2018), Valencia, Spain (2018)Google Scholar