Low-Latency Embedded AI Device for Vehicle-to-Vehicle Communication
Keywords:
Autonomous vehicles, Edge computing, Embedded artificial intelligence, Intelligent transportation systems, Low-latency communication, V2VAbstract
Vehicle-to-Vehicle (V2V) communication has emerged as a fundamental technology for enabling intelligent transportation systems, autonomous driving, and cooperative vehicular safety applications. However, conventional cloud-centric vehicular communication architectures often suffer from high communication latency, excessive bandwidth consumption, limited scalability, and delayed response times, which can adversely affect safety-critical driving operations. To address these challenges, this paper proposes a low-latency embedded Artificial Intelligence (AI) device for intelligent V2V communication that integrates embedded edge intelligence, real-time AI inference, adaptive wireless communication, cooperative perception, and lightweight cybersecurity mechanisms within a unified framework. The proposed architecture employs local sensor fusion and edge-based AI processing to perform real-time object detection, collision prediction, trajectory estimation, and intelligent decision-making directly within vehicles, thereby reducing dependence on remote cloud infrastructures. Furthermore, an adaptive V2V communication module utilizing intelligent packet prioritization and resource allocation is incorporated to enhance communication reliability and responsiveness in dynamic vehicular environments. The proposed system was evaluated using MATLAB simulations under varying vehicular mobility and communication conditions. Simulation results demonstrate an average end-to-end communication latency of approximately 12.3 ms, an average network throughput of 63.7 Mbps, and a packet delivery ratio of 97.4%, confirming the suitability of the framework for ultra-reliable low-latency vehicular communications. Additionally, the embedded AI framework achieved an average energy consumption of 1.9 J while reducing the collision risk score from approximately 0.9 during initial operation to below 0.25 under steady-state conditions. The system also maintained stable performance under increasing network load, supporting up to 28 active communication links without significant degradation in communication quality. These results demonstrate that the proposed embedded AI-enabled V2V architecture provides an effective, scalable, and energy-efficient solution for next-generation autonomous vehicles, smart transportation infrastructures, and future 5G/6G vehicular communication ecosystems.
References
M. A. Naeem, S. Chaudhary, and Y. Meng, “Road to Efficiency: V2V Enabled Intelligent Transportation System,” Electronics, vol. 13, no. 13, pp. 2673–2673, July 2024.
X. Zhang et al., “Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy,” Automotive Innovation, vol. 8, pp. 13–45, Feb. 2025.
S. Adnan Yusuf, A. Khan, and R. Souissi, “Vehicle-to-everything (V2X) in the autonomous vehicles domain – A technical review of communication, sensor, and AI technologies for road user safety,” Transportation Research Interdisciplinary Perspectives, vol. 23, no. 2590–1982, p. 100980, Jan. 2024.
F. Zhang and G. Wang, “Context-aware resource allocation for vehicle-to-vehicle communications in cellular-V2X networks,” Ad Hoc Networks, vol. 163, p. 103582, Oct. 2024.
P. Liang, W. Chen, H. Fan, and H. Zhu, “Leveraging Time-Critical Computation and AI Techniques for Task Offloading in Internet of Vehicles Network Applications,” Electronics, vol. 13, no. 16, p. 3334, Aug. 2024.
Y. Feng, H. Shen, Z. Shan, Q. Yang and X. Shi, "Semantic Communication for Edge Intelligence Enabled Autonomous Driving System," in IEEE Network, vol. 39, no. 2, pp. 149-157, March 2025.
M. I. Ashraf, Chen-Feng Liu, M. Bennis and W. Saad, "Towards low-latency and ultra-reliable vehicle-to-vehicle communication," 2017 European Conference on Networks and Communications (EuCNC), Oulu, Finland, 2017, pp. 1-5.
I. Khalid, V. Maglogiannis, D. Naudts, A. Shahid, and I. Moerman, “Optimizing Hybrid V2X Communication: An Intelligent Technology Selection Algorithm Using 5G, C-V2X PC5 and DSRC,” Future Internet, vol. 16, no. 4, p. 107, Apr. 2024.
S. Mande and N. Ramachandran, “A Comprehensive Survey on Challenges and Issues in V2X and V2V Communication in 6G Future Generation Communication Models,” Ingénierie des systèmes d information, vol. 29, no. 3, pp. 951–960, June 2024.
W. Ahmad, F. Ali, and A. Ali, “Scalable AI-driven intent-based vehicle-to-everything communications for secure, low-latency, and energy-efficient smart cities,” Vehicular Communications, vol. 56, p. 100973, Dec. 2025.
H. Lee, S. Kang, and H. Kim, “Causality-Sensitive Scheduling to Reduce Latency in Vehicle-to-Vehicle Interactions,” Sensors, vol. 24, no. 22, p. 7142, Nov. 2024.
J. M. Gimenez-Guzman, I. Leyva-Mayorga, and P. Popovski, “Semantic V2X Communications for Image Transmission in 6G Systems,” IEEE Network, vol. 38, no. 6, pp. 48–54, Nov. 2024.
J. Lu et al., "Generative Artificial intelligence-Enhanced MultiModal Semantic Communication in Internet of Vehicles: System Design and Methodologies," in IEEE Vehicular Technology Magazine, vol. 20, no. 2, pp. 71-82, June 2025.
T.-H. Wang et al., “V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction,” arXiv, 2020.
H. Hartenstein and L. P. Laberteaux, "A tutorial survey on vehicular ad hoc networks," in IEEE Communications Magazine, vol. 46, no. 6, pp. 164-171, June 2008.
C. Campolo, A. Molinaro, and Riccardo Scopigno, “From today’s VANETs to tomorrow’s planning and the bets for the day after,” Vehicular Communications, vol. 2, no. 3, pp. 158–171, July 2015.
K. Abboud, H. A. Omar and W. Zhuang, "Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey," in IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 9457-9470, Dec. 2016.
S. Chen, J. Hu, Y. Shi and L. Zhao, "LTE-V: A TD-LTE-Based V2X Solution for Future Vehicular Network," in IEEE Internet of Things Journal, vol. 3, no. 6, pp. 997-1005, Dec. 2016.
M. I. Ashraf, C.-F. Liu, M. Bennis, and W. Saad, “Towards low-latency and ultra-reliable vehicle-to-vehicle communication," 2017 European Conference on Networks and Communications (EuCNC), Oulu, pp. 1-5, 2017.
I. Soto, M. Calderon, O. Amador, and M. Urueña, “A survey on road safety and traffic efficiency vehicular applications based on C-V2X technologies,” Vehicular Communications, vol. 33, p. 100428, Jan. 2022.
Y. Sun, M. Peng, Y. Zhou, Y. Huang and S. Mao, "Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues," in IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3072-3108, 2019.
E. Ahmed and H. Gharavi, "Cooperative Vehicular Networking: A Survey," in IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 3, pp. 996-1014, March 2018.
D. Wang, A. Qiu, Q. Zhou, and H. D. Schotten, “A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications,” arXiv.org, 2025.
B. Saoud et al., “Artificial Intelligence, Internet of things and 6G methodologies in the context of Vehicular Ad-hoc Networks (VANETs): Survey,” ICT Express, vol. 10, no. 4, pp. 959–980, May 2024.
J. Morais, G. Charan, N. Srinivas and A. Alkhateeb, "DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset," in IEEE Transactions on Vehicular Technology, vol. 74, no. 11, pp. 17461-17476, Nov. 2025.
Lv Shengnan, Y. Qin, W. Gan, Z. Xu, and L. Shi, “A systematic literature review of vehicle-to-everything in communication, computation and service scenarios,” International Journal of General Systems, vol. 53, no. 7–8, pp. 1042–1072, May 2024.
K. B. Yogha Bintoro, SDH Permana, Ade Syahputra, Yaddarabullah, and Budi Arifitama, “V2V Communication in Smart Traffic Systems: Current status, challenges and future perspectives,” Jurnal PROCESSOR, vol. 19, no. 1, May 2024.
