Autonomous Vehicles (AV) are revolutionizing transportation, but real-time decision-making remains a
challenge due to End-To-End Delay (EED introduced by Cloud Computing (CC) based processing. A 5G-enabled Edge
Computing Model (5G-EECM) is proposed to address this problem by processing time-sensitive tasks at the network edge,
closer to the AV, reducing EED and improving responsiveness. The architecture uses Machine Learning (ML) for Obstacle
Detection (OD) and Reinforcement Learning (RL) for navigation, dynamically switching between Edge Computing (EC)
EC and CC based on task demands. The study tested the system using a user-friendly AV on a controlled track, revealing
increased response times, reduced average EED, reduced energy consumption, and improved OD accuracy. The results
demonstrate that 5G-EECM significantly boosts AV systems' real-time safety and efficiency, making it reliable and
scalable for next-generation AV systems.
S. M. Hosseinian and H. Mirzahossein, “Efficiency and Safety of Traffic Networks Under the Effect of Autonomous Vehicles,” Iranian Journal of Science and Technology, Transactions of Civil Engineering, vol. 48, no. 4, pp. 1861–1885, Dec. 2023, doi: 10.1007/s40996-023-01291-8.
G. Bathla et al., “Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities,” Mobile Information Systems, vol. 2022, pp. 1–36, Jun. 2022, doi: 10.1155/2022/7632892.
Y. Ma, Z. Wang, H. Yang, and L. Yang, “Artificial intelligence applications in the development of autonomous vehicles: a survey,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 2, pp. 315–329, Mar. 2020, doi: 10.1109/jas.2020.1003021.
H. S. M. Lim and A. Taeihagh, “Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities,” Sustainability, vol. 11, no. 20, p. 5791, Oct. 2019, doi: 10.3390/su11205791.
M. Sadaf et al., “Connected and Automated Vehicles: Infrastructure, Applications, Security, Critical Challenges, and Future Aspects,” Technologies, vol. 11, no. 5, p. 117, Sep. 2023, doi: 10.3390/technologies11050117.
R. Ekatpure, “Enhancing Autonomous Vehicle Performance through Edge Computing: Technical Architectures, Data Processing, and System Efficiency,” Applied Research in Artificial Intelligence and Cloud Computing, 6(11), 17-34, (2023).
H. Khayyam, B. Javadi, M. Jalili, and R. N. Jazar, “Artificial Intelligence and Internet of Things for Autonomous Vehicles,” Nonlinear Approaches in Engineering Applications, pp. 39–68, Aug. 2019, doi: 10.1007/978-3-030-18963-1_2.
G. Khan et al., “Energy‐Efficient Routing Algorithm for Optimizing Network Performance in Underwater Data Transmission Using Gray Wolf Optimization Algorithm,” Journal of Sensors, vol. 2024, no. 1, Jan. 2024, doi: 10.1155/2024/2288527.
A. H. A. AL-Jumaili, R. C. Muniyandi, M. K. Hasan, J. K. S. Paw, and M. J. Singh, “Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations,” Sensors, vol. 23, no. 6, p. 2952, Mar. 2023, doi: 10.3390/s23062952.
B. Gao, J. Liu, H. Zou, J. Chen, L. He, and K. Li, “Vehicle-Road-Cloud Collaborative Perception Framework and Key Technologies: A Review,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 12, pp. 19295–19318, Dec. 2024, doi: 10.1109/tits.2024.3459799.
S. B. Kamtam, Q. Lu, F. Bouali, O. C. L. Haas, and S. Birrell, “Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies,” Sensors, vol. 24, no. 12, p. 3957, Jun. 2024, doi: 10.3390/s24123957.
A. C. S. Robert Vincent and S. Sengan, “Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data,” Scientific Reports, vol. 14, no. 1, Sep. 2024, doi: 10.1038/s41598-024-71726-3.
Y. Kwon, W. Kim, and I. Jung, “Neural Network Models for Driving Control of Indoor Autonomous Vehicles in Mobile Edge Computing,” Sensors, vol. 23, no. 5, p. 2575, Feb. 2023, doi: 10.3390/s23052575.
Z. Lin, H. Cui, and Y. Liu, “Distributed Deep Learning Based on Edge Computing Over Internet of Vehicles: Overview, Applications, and Challenges,” IEEE Access, vol. 12, pp. 133734–133747, 2024, doi: 10.1109/access.2024.3454790.
Z. Ghaleb Al-Mekhlafi et al., “Coherent Taxonomy of Vehicular Ad Hoc Networks (VANETs) Enabled by Fog Computing: A Review,” IEEE Sensors Journal, vol. 24, no. 19, pp. 29575–29602, Oct. 2024, doi: 10.1109/jsen.2024.3436612.
X. Tan and Y. Zhang, “A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles,” Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 66, no. 5, pp. 1583–1599, Dec. 2022, doi: 10.1177/00187208221143028.
K. Sathupadi, “An AI-driven framework for dynamic resource allocation in software-defined networking to optimize cloud infrastructure performance and scalability,” International Journal of Intelligent Automation and Computing, vol. 6, no. 1, pp. 46-64, 2023.
A. Santoso, & Y. Surya, “Maximizing Decision Efficiency with Edge-Based AI Systems: Advanced Strategies for Real-Time Processing, Scalability, and Autonomous Intelligence in Distributed Environments. Quarterly Journal of Emerging Technologies and Innovations, vol. 9, no. 2, 104-132, 2024.
S. Chen, X. Hu, J. Zhao, R. Wang, and M. Qiao, “A Review of Decision-Making and Planning for Autonomous Vehicles in Intersection Environments,” World Electric Vehicle Journal, vol. 15, no. 3, p. 99, Mar. 2024, doi: 10.3390/wevj15030099.
S. Panneerselvam, S. K. Thangavel, V. S. Ponnam, and S. Sengan, “Federated learning based fire detection method using local MobileNet,” Scientific Reports, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-82001-w.
S. Dasgupta, X. Zhu, M. Sami Irfan, M. Rahman, J. Gong, and S. Jones, “AI machine vision for safety and mobility: an autonomous vehicle perspective,” Handbook on Artificial Intelligence and Transport, pp. 380–409, Oct. 2023, doi: 10.4337/9781803929545.00023.
E. Yazdani Bejarbaneh, H. Du, and F. Naghdy, “Exploring Shared Perception and Control in Cooperative Vehicle-Intersection Systems: A Review,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 11, pp. 15247–15272, Nov. 2024, doi: 10.1109/tits.2024.3432634.
S. G. G. K et al., “Optimizing the cyber-physical intelligent transportation system network using enhanced models for data routing and task scheduling,” Digital Communications and Networks, Jan. 2025, doi: 10.1016/j.dcan.2025.01.004.
N. S. Alsharafa, S. Sengan, S. S. T, A. D, S. V, and R. K, “An Edge Assisted Internet of Things Model for Renewable Energy and Cost-Effective Greenhouse Crop Management,” Journal of Machine and Computing, pp. 576–588, Jan. 2025, doi: 10.53759/7669/jmc202505045.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Geethanjali D, Meena Rani N, Prasanna Kumar Lakineni, Veeraiah Maddu, Abhilash S Nath and Tamil Thendral M;
Methodology: Geethanjali D, Meena Rani N and Prasanna Kumar Lakineni;
Writing- Original Draft Preparation: Geethanjali D, Meena Rani N, Prasanna Kumar Lakineni, Veeraiah Maddu, Abhilash S Nath and Tamil Thendral M;
Supervision: Geethanjali D, Meena Rani N and Prasanna Kumar Lakineni;
Validation: Veeraiah Maddu, Abhilash S Nath and Tamil Thendral M;
Writing- Reviewing and Editing: Geethanjali D, Meena Rani N, Prasanna Kumar Lakineni, Veeraiah Maddu, Abhilash S Nath and Tamil Thendral M;
All authors reviewed the results and approved the final version of the manuscript.
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Corresponding author
Geethanjali D
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
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Cite this article
Geethanjali D, Meena Rani N, Prasanna Kumar Lakineni, Veeraiah Maddu, Abhilash S Nath and Tamil Thendral M, “Real-Time Autonomous Vehicle Automation with 5G-Based Edge Computing and Artificial Intelligence”, Journal of Machine and Computing, pp. 1084-1098, April 2025, doi: 10.53759/7669/jmc202505086.
