saucis Sakarya University Journal of Computer and Information Sciences 2636-8129 Sakarya University 10.35377/saucis...1666618 Automation Engineering Otomasyon Mühendisliği Enhancing Autonomous Vehicle Safety Through Chaid Modeling: Influential Factors, Seasonal Variations, and Systematic Limitations https://orcid.org/0000-0002-1879-9215 Baş Kaman Ferhan ANKARA YILDIRIM BEYAZIT UNIVERSITY https://orcid.org/0000-0002-2364-9449 Yücel Ahmet ANKARA YILDIRIM BEYAZIT UNIVERSITY 12 29 2025 8 4 718 739 03 27 2025 10 16 2025 Copyright © 2018, Sakarya University Journal of Computer and Information Sciences 2018 Sakarya University Journal of Computer and Information Sciences

The growing prominence of artificial intelligence has driven transformative innovations across sectors, with autonomous vehicles representing a salient manifestation of this technological shift. The reliability of autonomous vehicles plays a crucial role in determining their societal acceptance and large-scale deployment. Within this context, disengagement data serve as an objective indicator of system reliability. A rigorous analysis of disengagement data is essential for evaluating the real-world performance and operational reliability of autonomous vehicles. Such data circumstances necessitate human intervention, thereby revealing system vulnerabilities and opportunities for improvement. Consequently, precise and transparent disengagement analyses are vital for advancing AV technology and strengthening safety. This study investigates the determinants of disengagements and contrasts human-initiated with system-initiated events. Drawing on 17,406 reports (2021–2023), CHAID models identified key triggers including environmental context, system limitations, and operational conditions. The study identified key determinants, including planning inconsistencies, detection failures, and hardware malfunctions, and revealed clear seasonal variations, with disengagements peaking in summer and autumn and declining in winter and spring. Validated CHAID models demonstrated high accuracy, underscoring the importance of comprehensive training and testing across diverse conditions to enhance effectiveness and safety.

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