Data driven vehicular heterogeneity based intelligent collision avoidance system for Internet of Vehicles (IoV)✩

Iqra Adnan; Tariq Umer; Ahmad Arsalan; Maryam M. Al Dabel; Ali Kashif Bashir; Arooj Ansif

doi:10.1016/j.dcan.2025.03.010

›› 2026, Vol. 12 ›› Issue (1) :180 -197. DOI: 10.1016/j.dcan.2025.03.010

Special issue on cyber-physical systems for intelligent transportation and smart cities

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Data driven vehicular heterogeneity based intelligent collision avoidance system for Internet of Vehicles (IoV)^✩

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^a Department of Computer Science, COMSATS University Islamabad, Lahore campus, Pakistan

^b Department of CS & IT, The University of Lahore, Lahore, Pakistan

^c Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, Pakistan

^d Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hafr Al Batin, Saudi Arabia

^e Department of Computing and Mathematics, Manchester Metropolitan University, UK

^f Department of Computer Science & Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates

^g Department of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK

^* tariqumer@cuilahore.edu.pk (T. Umer), Dr.alikashif.b@ieee.org (A.K. Bashir).

Iqra Adnan received an M.S. degree in Computer Science from the COMSATS Univer-sity Islamabad, Lahore campus, Lahore, Pakistan in 2023, and is currently doing a PhD in Computer Science from COMSATS University, Lahore Campus, Pakistan. Besides this, she is working as a full-time Lecturer in the Faculty of Information Technology and Computer Science at the University of Lahore, Lahore, Pakistan. His research interests include de-signing and developing digital twins for future Internet architectures, cross-layer design for wireless networks, cooperation between federated learning and future networks, and eﬃcient forwarding and routing protocols for Internet of Vehicles & IoT. She also reviews IEEE ACCESS and IEEE conferences.

Tariq Umer received his Ph.D. in Communication systems in 2012 from the School of Computing & Communication, Lancaster University, U.K, and his Master in Computer Science in 1997 from Bahauudin Zakariya University, Multan, Pakistan. He served the IT education sector in Pakistan for more than 20 years. He joined COMSATS University in January 2007, as an Assistant Professor in the CS department of COMSATS University Is-lamabad, Lahore Campus, Lahore. Currently, he is working as Associate Professor (TTS) at CUI, Lahore Campus. His research interests include Vehicular Ad-hoc Networks, the Inter-net of Things, Digital Twins, Edge Computing and Telecommunication Network Design. He served in the TPC for various international conferences. He is currently an Associate Editor of IEEE ACCESS and served as Editorial team member for Future Generation Com-puter Systems (Elsevier) for 10 years. He is serving as a reviewer for IEEE Communications Letters, IEEE Wireless Communication Magazine, IEEE Network, IEEE ACCESS, Comput-ers and Electrical Engineering (Elsevier), Journal of Network and Computer Applications (Elsevier), Ad Hoc Sensor Wireless Networks, Wireless Networks (Springer) journal, and the MDPI Journals. He is an active member of the Pakistan Computer Society and Internet Society Pakistan.

Ahmad Arsalan received an M.S. degree in Computer Science from the National Uni-versity of Computer and Emerging Sciences, Islamabad, Pakistan, in 2018, and is currently doing a PhD in Computer Science from COMSATS University, Lahore Campus, Pakistan. Besides this, he is working as a full-time Senior Lecturer in the Faculty of Information Technology and Computer Science at the University of Central Punjab, Lahore, Pakistan. His research interests include designing and developing digital twins for future Internet architectures, cross-layer design for wireless networks, cooperation between federated learning and future networks, and eﬃcient forwarding and routing protocols for named data networking-based software-defined networks. He also reviews IEEE ACCESS, Elsevier Journal of Future Generation Computer Systems, and IEEE Transactions on Reliability.

Maryam M. Al Dabel received the Ph.D. degree in computer science from the Uni-versity of Sheﬃeld, Sheﬃeld, U.K., in 2016. She is currently an Assistant Professor of artificial intelligence and machine learning with the College of Computer Science and En-gineering, University of Hafr Al Batin, Hafar Al-Batin, Saudi Arabia, where she is also a Vice Dean with Computer Science and Engineering College.

Ali Kashif Bashir (Senior Member, IEEE) is Chair Professor of Computer Networks and Cybersecurity at Manchester Metropolitan University, UK. He leads the SISTEMS: Secure and Intelligent Systems Research Group, the PriSe and AI-Sec Labs, the Turing Network’s AI Safety and Security Taskforce, and the cybersecurity pathway’s line manage-ment. He has held honorary or adjunct roles in the US, Canada, China, Malaysia, Lebanon, the UAE, India, and Pakistan, and has advised government bodies and major companies, including IBM, Siemens, and Samsung. Ali earned his PhD from Korea University in 2012. He has published 450+ articles, with over 17.5K citations and an h-index of 71, and has been featured by the IEEE and cited by organizations such as the IMF, the European Union, and the governments of Singapore and Denmark. He has produced multiple patents and products, managed £10M in strategic funds, secured over £4M in research grants, deliv-ered 100+ invited/keynote talks, and chaired 50+ conferences and workshops. He is an ACM Distinguished Speaker, a Clarivate Highly Cited Researcher (2023-24), and Editor-in-Chief of IEEE Technology, Policy and Ethics. He also serves in senior editorial roles with IEEE Transactions on Consumer Electronics and several other leading journals.

Arooj Ansif is currently pursuing the Ph.D. degree in engineering with the University of Huddersfield, U.K. Her academic journey spans esteemed institutions across Pakistan and the U.K. She has been a Lecturer in computer science with the University of Educa-tion, Lahore, since 2009 (currently on study leave). She has published in leading scientific journals, with impactful research in machine learning, transportation networks, the In-ternet of Vehicles (IoV), blockchain, and computer-aided diagnostics. Her works appear in high-impact journals, such as IEEE Access, Entropy, Sensors, and Journal of Network and Computer Applications. Her research focuses on advanced connected technologies for electric vehicle safety, blending innovation, interdisciplinary collaboration, and real-world application. She has received recognitions, including the Engineering Excellence Scholarship (University of Huddersfield), selected as a Young Researcher at the Heidel-berg Laureate Forum, and a Travel Grant from IEEE/GIST South Korea.

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Abstract

The Internet of Vehicles (IoV) is an emerging technology that aims to connect vehicles, infrastructure, and other devices to enable intelligent transportation systems. One of the key challenges in IoV is to ensure safe and eﬃcient communication among vehicles of diﬀerent types and capabilities. This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV. The system leverages Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication to collect real-time data about the environment and the vehicles. The data is collected to acknowledge the heterogeneity of vehicles and human behavior. The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions. The system takes into account the heterogeneity of vehicles, such as their size, speed, and maneuverability, to optimize collision avoidance strategies. The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems. The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and eﬃciency of IoV with an accuracy of 96.5% using the SVM algorithm. The trial outcomes demonstrated that the new system, incorporating vehicular, weather, and human behavior factors, outperformed previous systems that only considered vehicular and weather aspects. This innovative approach is poised to lead transportation eﬀorts, reducing accident rates and improving the quality of transportation systems in smart cities. By oﬀering predictive capabilities, the proposed model not only helps control accident rates but also prevents them in advance, ensuring road safety.

Keywords

Internet of Vehicles / Collision avoidance / Machine learning / Traﬃc safety / Autonomous vehicles / Vehicular networks / Vehicular heterogeneity / Smart transportation / Traﬃc modeling

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Iqra Adnan, Tariq Umer, Ahmad Arsalan, Maryam M. Al Dabel, Ali Kashif Bashir, Arooj Ansif. Data driven vehicular heterogeneity based intelligent collision avoidance system for Internet of Vehicles (IoV)^✩. , 2026, 12(1): 180-197 DOI:10.1016/j.dcan.2025.03.010

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CRediT authorship contribution statement

Iqra Adnan: Investigation, Formal analysis. Tariq Umer: Supervi-sion. Ahmad Arsalan: Software, Methodology. Maryam M. Al Dabel: Writing-review & editing, Investigation. Ali Kashif Bashir: Valida-tion, Supervision, Resources. Arooj Ansif: Writing-review & editing, Methodology, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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