Visible light communication for vehicle-to-vehicle systems: a deep neural network-based signal detection framework
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Archana Jadhav
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Dipali Himmatrao Patil
Abstract
Visible light communication (VLC) is emerging as a promising alternative to conventional radio frequency-based vehicle-to-vehicle (V2V) communication due to its high data rate, immunity to electromagnetic interference, and use of energy-efficient LED-based vehicular lighting systems. However, signal detection in dynamic V2V-VLC environments remains a significant challenge due to factors such as high mobility, channel fading, ambient light interference, and misalignment between transmitter and receiver. This paper proposes a robust deep neural network (DNN) architecture specifically designed for accurate and reliable signal detection in V2V-VLC systems. Through extensive simulations and experimental evaluations under varying vehicular conditions, our framework achieves superior detection accuracy of 99.8 % at a distance of 1 m and 98.4 % at 5 m while exhibiting greater resilience compared to traditional signal processing methods. The research aims to enhance communication reliability in intelligent transportation systems, paving the way for safer and more efficient autonomous driving environments.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: Only use of the gramtical corrections and sentence improvement.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
References
1. Lin, Y, Wang, P, Ma, M. Intelligent transportation system (ITS): concept, challenge and opportunity. In: 2017 IEEE 3rd international conference on big data security on cloud (bigdatasecurity), IEEE international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids). Beijing, China: IEEE; 2017:167–72 pp.10.1109/BigDataSecurity.2017.50Search in Google Scholar
2. Darbha, S, Konduri, S, Pagilla, PR. Benefits of V2V communication for autonomous and connected vehicles. IEEE Trans Intell Transport Syst 2018;20:1954–63. https://doi.org/10.1109/tits.2018.2859765.Search in Google Scholar
3. Nguyen, TV, Shailesh, P, Sudhir, B, Kapil, G, Jiang, L, Wu, Z, et al.. A comparison of cellular vehicle-to-everything and dedicated short range communication. In: 2017 IEEE vehicular networking conference (VNC). Turin, Italy: IEEE; 2017:101–8 pp.10.1109/VNC.2017.8275618Search in Google Scholar
4. Chen, S, Hu, J, Zhao, L, Zhao, R, Fang, J, Shi, Y, et al.. Cellular vehicle-to-everything (C-V2X). Berlin/Heidelberg, Germany: Springer Nature; 2023.10.1007/978-981-19-5130-5Search in Google Scholar
5. Matheus, LEM, Borges Vieira, A, Vieira, LFM, Vieira, MAM, Gnawali, O. Visible light communication: concepts, applications and challenges. Commun Surv Tutorials, IEEE 2019;21:3204–37. https://doi.org/10.1109/comst.2019.2913348.Search in Google Scholar
6. Pathak, PH, Feng, X, Hu, P, Mohapatra, P. Visible light communication, networking, and sensing: a survey, potential and challenges. Commun Surv Tutorials, IEEE 2015;17:2047–77. https://doi.org/10.1109/comst.2015.2476474.Search in Google Scholar
7. França, RP, Borges Monteiro, AC, Arthur, R, Iano, Y. An overview of deep learning in big data, image, and signal processing in the modern digital age. In: Trends in deep learning methodologies. Massachusetts, USA: Academic Press; 2021:63–87 pp.10.1016/B978-0-12-822226-3.00003-9Search in Google Scholar
8. Sefako, T, Yang, F, Song, J, Balmahoon, R, Cheng, L. A review of machine learning techniques for optical wireless communication in intelligent transport systems. Intell Converg Netw 2024;99:1–33. https://doi.org/10.23919/icn.2024.0019.Search in Google Scholar
9. Khan, AR, Jamlos, MF, Osman, N, Ishak, MI, Dzaharudin, F, Kok Yeow, Y, et al.. DSRC technology in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) IoT system for intelligent transportation system (ITS): a review. In: Recent trends in mechatronics towards industry 4.0: selected articles from iM3F 2020, Malaysia. Singapore: Springer; 2021:97–106 pp.10.1007/978-981-33-4597-3_10Search in Google Scholar
10. Shurrab, M, Singh, S, Otrok, H, Mizouni, R, Khadkikar, V, Zeineldin, H. An efficient vehicle-to-vehicle (V2V) energy sharing framework. IEEE Internet Things J 2021;9:5315–28. https://doi.org/10.1109/jiot.2021.3109010.Search in Google Scholar
11. Tahir, MN, Leviäkangas, P, Katz, M. Connected vehicles: V2V and V2I road weather and traffic communication using cellular technologies. Sensors 2022;22:1142. https://doi.org/10.3390/s22031142.Search in Google Scholar PubMed PubMed Central
12. Ngo, H, Fang, H, Wang, H. Cooperative perception with V2V communication for autonomous vehicles. IEEE Trans Veh Technol 2023;72:11122–31. https://doi.org/10.1109/tvt.2023.3264020.Search in Google Scholar
13. Sharda, P, Reddy, GS, Bhatnagar, MR, Zabih, G. A comprehensive modeling of vehicle-to-vehicle based VLC system under practical considerations, an investigation of performance, and diversity property. IEEE Trans Commun 2022;70:3320–32. https://doi.org/10.1109/tcomm.2022.3158325.Search in Google Scholar
14. Chaabna, A, Babouri, A, Chouabia, H, Hafsi, T, Meguetta, ZE, Zhang, X. Experimental demonstration of v2v communication system based on vlc technology for smart transportation. In: International conference on artificial intelligence in renewable energetic systems. Cham: Springer International Publishing; 2021:653–61 pp.10.1007/978-3-030-92038-8_65Search in Google Scholar
15. Mishra, S, Maheshwari, R, Grover, J, Vaishnavi, V. Investigating the performance of a vehicular communication system based on visible light communication (VLC). Int J Inf Technol 2022;14:877–85. https://doi.org/10.1007/s41870-021-00834-4.Search in Google Scholar
16. Kumar, CS, Jeyachitra, RK. Performance enhancement of vehicle-to-vehicle (V2V) distance measurement using ceramic fiber optic VLC. J Ceram Process Res 2025:313–22.Search in Google Scholar
17. Hasnawi, A, Rasha, Marghescu, I, Rusu-Casandra, A. Reliability and capacity evaluation for vehicle-to-vehicle VLC. In: 2024 15th International conference on communications (COMM). IEEE; 2024:1–6 pp.10.1109/COMM62355.2024.10741390Search in Google Scholar
18. Hasnawi, A, Rasha, Ioana Marghescu, C, and Rusu-Casandra, A. Enhancing vehicular VLC systems with multi-relay techniques: a performance evaluation. Electronics 2025;14:1170, https://doi.org/10.3390/electronics14061170.Search in Google Scholar
19. El, S, Noura, H, Soliman, SS, Abdellatif, MM. Performance analysis of VLC-based infrastructure-to-vehicle communication. In: 2023 International telecommunications conference (ITC-Egypt). Alexandria, Egypt: IEEE; 2023:248–53 pp.10.1109/ITC-Egypt58155.2023.10206363Search in Google Scholar
20. Sharda, P. Next generation based vehicular visible light communications: a novel transmission scheme. IEEE Trans Veh Technol 2024;73:16735–43. https://doi.org/10.1109/tvt.2024.3417479.Search in Google Scholar
21. Srivastava, MK. VLC-based collision avoidance system for vehicles on curved roads in hilly areas. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0132.Search in Google Scholar
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