eISSN: 3087-4319 editor@aimjournals.com
All Journals
Submit your article

International Journal of Next-Generation Engineering and Technology

Open Access Peer Review International
Open Access

Advanced Evolutionary Optimization and Intelligent Sensor Integration for Electromagnetic Compatibility and Signal Integrity in Autonomous Vehicle Architectures

DOI
Evan Richman 1 ,
4 Institute for Advanced Automotive Systems, University of Stuttgart, Germany

Abstract

The rapid evolution of autonomous driving technologies and the proliferation of high-voltage power electronics have introduced unprecedented challenges in electromagnetic compatibility (EMC) and signal integrity. This study provides a comprehensive investigation into the integration of Advanced Driver Assistance Systems (ADAS) with evolutionary fuzzy logic and high-speed data acquisition frameworks. By synthesizing nature-inspired modeling techniques, such as genetic fuzzy systems and differential evolution, the research addresses the complexities of vehicle-level EMC design for automotive inverters and high-speed Ethernet communication. The study specifically evaluates the performance of 10G automotive Ethernet through HyperLynx-validated shielding methodologies for camera PCB design in lighting control modules. Furthermore, the paper explores the role of on-board diagnostics and panoramic imaging systems in enhancing situational awareness while mitigating common-mode noise propagation in four-wheel-drive electric vehicles. The methodology combines prospective and retrospective performance assessments with advanced video compression strategies to ensure real-time streaming capabilities without compromising data fidelity. Results indicate that the application of evolutionary fuzzy rule forests and symbolic regression significantly improves the predictive accuracy of vehicle flow and sensor interference detection. The research concludes that a holistic approach, blending intelligent computational paradigms with robust hardware shielding, is essential for the sustainable development of the next generation of interconnected, autonomous, and electromagnetically resilient vehicular platforms.

Keywords

ADAS, Electromagnetic Compatibility, Evolutionary Fuzzy Systems, Automotive Ethernet

References

Funato, H., Li, J. and Takahashi, M. (2015). Vehicle-level analysis technique for EMC design of automotive inverters. Hitachi Review 64, 8, 501-505.
Gao, F., Ye, C. K., Wang, Z. L. and Li, X. (2018). Improvement of low frequency radiated emission in electric vehicle by numerical analysis. J. Control Science and Engineering, 2018, Article ID 5956973.
Gulino, M.S., Fiorentino, A., D. Vangi (2022). Prospective and retrospective performance assessment of advanced driver assistance systems in imminent collision scenarios: the CMI-Vr approach. Eur. Transp. Res. Rev., 14 (1), 10.1186/s12544-022-00527-4.
Huang, T.-Y., Wang, Y.-C., Liao, C.-J., Sang, I.-C. (2017). Panoramic vehicular imaging system. Proc. SPIE-Int. Soc. Opt. Eng., 10209, 10.1117/12.2268569.
Idir, N., Weens, Y., Moreau, M. and Franchaud, J. J. (2009). High-frequency behavior models of ac motors. IEEE Trans. Magnetics 45, 1, 133-138.
Ivan, E., Fernando, A., Mateo, J., Alvaro, P., Javier, P. and Francisco, J. A. (2018). Common mode noise propagation and effects in a four-wheel driven electric vehicle. IEEE Trans. Electromagnetic Compatibility 60, 1, 132-139.
Jahani Moghaddam, M. (2024). A survey on genetic fuzzy systems. Arch. Comput. Methods Eng., 10.1007/s11831-024-10157-9.
Jeong, I. S., Choi, H. W., Choi, H. S. and Chung, D. C. (2018). Analysis of s-parameter using different materials for the WPT resonance coil. IEEE Trans. Applied Superconductivity 28, 3, 1-5.
Jeschke, S., Hirsch, H., Trautmann, M. and Maarleveld, M. (2016). EMI measurement on electric vehicle drive inverters using a passive motor impedance network. Proc. Asia-Pacific Int. Symp. Electromagnetic Compatibility (APEMC), Shenzhen, China.
Jin, X. L., Gu, X. L. and Li, B. (2018). Scattering parameter simulation of microwave window with conformai FDTD method. Proc. IEEE Int. Vacuum Electronics Conf. (IVEC), Monterey, California, USA.
Jose, H., Artur N., Jose, S. and Magno, A. (2016). Proposal for a Brazilian regulation of electromagnetic compatibility applied to automotive vehicles. Proc. IEEE Int. Symp. Electromagnetic Compatibility, Ottawa, Canada.
Kalmeshwar, M., Nandini Prasad, K. (2018). Development of on-board diagnostics for car and it’s integration with android mobile. 2nd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2017, 10.1109/CSITSS.2017.8447540.
KARIM, A. S. A. (2025). Mitigating electromagnetic interference in 10G automotive Ethernet: hyperLynx-validated shielding for camera PCB design in ADAS lighting control. International Journal of Applied Mathematics, 38(2s), 1257-1268.https://doi.org/10.12732/ijam.v38i2s.718.
Kim, D., Hong, K., Jung, B., Kim, J. (2016). A real-time streaming system for a multifunctional vehicle information display based on TMO. Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015, pp. 210-215, 10.1109/KSE.2015.40.
Kolak, I., Lukac, Z., Knezic, M., Koncar, S. (2020). Realization of automotive video data acquisition system for usage in evolution of autonomous vehicles. 2020 Zooming Innovation in Consumer Technologies Conference, ZINC 2020, pp. 160-164, 10.1109/ZINC50678.2020.9161439.
Konecny, J. (2023). MJPEG Video Dataset, 10.17632/3ymvn9dm7x.1.
Kong, D.-W., Lee, S.-H. (2019). Study of wireless communication technology between LED-based vehicles using CSK method. Int. J. Recent. Technol. Eng., 7 (6), pp. 1231-1234.
Koza, J.R. (1990). Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems: Technical Report STAN-CS-90-1314. Dept. of Computer Science, Stanford University.
Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.
Krömer, P., Hasal, M., Nowaková, J., Heckenbergerova, J., Musílek, P. (2020). Statistical and nature-inspired modeling of vehicle flows by using finite mixtures of simple circular normal distributions. IEEE Intell. Transp. Syst. Mag., 12 (4), pp. 182-194, 10.1109/MITS.2020.3014419.
Krömer, P., Owais, S.S.J., Platos, J., Snásel, V. (2013). Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression. Comput. Math. Appl., 66 (2), pp. 190-200, 10.1016/j.camwa.2013.02.017.
Krömer, P., Platoš, J. (2017). Simultaneous prediction of wind speed and direction by evolutionary fuzzy rule forest. Procedia Comput. Sci., 108, pp. 295-304, 10.1016/j.procs.2017.05.195.
Kromer, P., Platos, J., Snasel, V., Abraham, A. (2011). Fuzzy classification by evolutionary algorithms. Systems, Man, and Cybernetics (SMC), 2011 IEEE Int. Conf. on, pp. 313-318, 10.1109/ICSMC.2011.6083684.
Li, X., Lin, K.-Y., Meng, M., Li, X., Li, L., Hong, Y., Chen, J. (2022). A survey of ADAS perceptions with development in China. IEEE Trans. Intell. Transp. Syst., 23 (9), pp. 14188-14203, 10.1109/TITS.2022.3149763.
Liu, B.-D., Huang, C.-Y. (1997). Design and implementation of the tree-based fuzzy logic controller. IEEE Trans. Syst. Man Cybern. B, 27 (3), pp. 475-487, 10.1109/3477.584954.
Moral, J., Castiello, C., Magdalena, L., Mencar, C. (2021). Explainable fuzzy systems: Paving the way from interpretable fuzzy systems to explainable AI systems. Studies in Computational Intelligence, Springer International Publishing.
Nafea, A.A., Alameri, S.A., Majeed, R.R., Khalaf, M.A., AL-Ani, M.M. (2024). A short review on supervised machine learning and deep learning techniques in computer vision. Babylon. J. Mach. Learn., 2024, pp. 48-55, 10.58496/BJML/2024/004.
Park, W., Choi, D., Lee, K. (2018). Threat analysis of Wi-Fi connected dashboard camera. 2018 International Conference on Platform Technology and Service, PlatCon 2018, 10.1109/PlatCon.2018.8472768.
Park, K.B., Kim, H.K. (2025). Field testing and detection of camera interference for autonomous driving. Lecture Notes in Computer Science, 15499 LNCS, pp. 191-202, 10.1007/978-981-96-1624-4_15.
Patra, S., Calafate, C.T., Cano, J.-C., Veelaert, P., Philips, W. (2017). Integration of vehicular network and smartphones to provide real-time visual assistance during overtaking. Int. J. Distrib. Sens. Netw., 13 (12), 10.1177/1550147717748114.
Pawlowski, P., Piniarski, K., Dabrowski, A. (2018). Selection and tests of lossless and lossy video codecs for advanced driver-assistance systems. In: Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA, Vol. 2018-September. pp. 344-349.
Pawłowski, P., Piniarski, K., Dąbrowski, A. (2021). Highly efficient lossless coding for high dynamic range red, clear, clear, clear image sensors. Sensors (Switzerland), 21 (2), pp. 1-17, 10.3390/s21020653.
Peled, D., Shmilovici, A., Hadar, O. (2023). Automative video compression for remote driving via safety considerations. 2023 IEEE Conference on Standards for Communications and Networking, CSCN 2023, pp. 54-58, 10.1109/CSCN60443.2023.10453135.
Prauzek, M., Krömer, P., Rodway, J., Musilek, P. (2016). Differential evolution of fuzzy controller for environmentally-powered wireless sensors. Appl. Soft Comput., 48, pp. 193-206, 10.1016/j.asoc.2016.06.040.
Precup, R.-E., Hellendoorn, H. (2011). A survey on industrial applications of fuzzy control. Comput. Ind., 62 (3), pp. 213-226, 10.1016/j.compind.2010.10.001.
Price, K.V., Storn, R.M., Lampinen, J.A. (2005). Differential evolution a practical approach to global optimization. Natural Computing Series, Springer-Verlag, Berlin, Germany.
van Rijsbergen, C.J. (1979). Information Retrieval (second ed.), Butterworths, London.
Russo, M. (2024). Fuzzy Learning and Applications, International Series on Computational Intelligence, CRC Press.
Salkind, N. (2006). Encyclopedia of Measurement and Statistics, SAGE Publications.
Salman, H.A., Kalakech, A. (2024). Image enhancement using convolution neural networks. Babylon. J. Mach. Learn., 2024, pp. 30-47, 10.58496/BJML/2024/003.
Sammut, C., Webb, G.I. (2011). Encyclopedia of Machine Learning (first ed.), Springer Publishing Company.

Similar Articles

51-60 of 80

You may also start an advanced similarity search for this article.