A new technical paper titled “Advances in You Only Look Once (YOLO) algorithms for lane and object detection in autonomous vehicles” was published by RMIT University, Kyungpook National University, Deakin University and the RCA Robotics Laboratory, Royal College of Art.
Abstract
“Ensuring the safety and efficiency of Autonomous Vehicles (AVs) necessitates highly accurate perception, especially for lane detection and lane-change manoeuvres. Among object detection frameworks, “You Only Look Once” (YOLO) algorithms have emerged as prominent contenders due to their rapid inference and commendable accuracy. However, the broad spectrum of YOLO variants and their applications in complex, real-world environments remain insufficiently mapped, necessitating a more integrative and critical perspective than what is typically offered by surveys. This comprehensive review synthesizes theoretical foundations, architectural innovations, and empirical evaluations of YOLO-based algorithms in AV-related tasks. It not only highlights key findings—such as the notable gains in real-time detection and adaptability to a range of driving conditions—but also explicitly identifies persistent gaps and limitations. These include difficulties in detecting subtle or degraded lane markings, handling unpredictable environmental factors like adverse weather and varied lighting, mitigating adversarial perturbations, and scaling effectively across diverse datasets and geographic regions. By critically examining these vulnerabilities, we illuminate the opportunities for refining YOLO’s training paradigms, optimizing model architectures, incorporating sensor fusion, and fostering universally applicable datasets. The implications of addressing these gaps extend beyond mere technical refinements. Proactively tackling YOLO’s current challenges can expedite the realization of safer, more robust, and globally adaptable AV navigation systems. In doing so, this review provides clear, actionable insights for researchers, engineers, and policymakers, guiding them toward strategic innovations that will strengthen AV perception and contribute to more reliable, future-ready transportation solutions.”
Find the technical paper here. February 2026.
Omodaratan, Busuyi, Ali Jamali, Timothy Wiley, Ziad Al-Saadi, Rammohan Mallipeddi, Ehsan Asadi, Hoshyar Asadi, Rasoul Sadeghian, Sina Sareh, and Hamid Khayyam. “Advances in You Only Look Once (YOLO) algorithms for lane and object detection in autonomous vehicles.” Engineering Applications of Artificial Intelligence 168 (2026): 113893. License.
Leave a Reply