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Dergiler / Sakarya University Journal of Computer and Information Sciences / 2025 / Volume: 8 Issue: 4

Enhancing Autonomous Vehicle Safety Through Chaid Modeling: Influential Factors, Seasonal Variations, and Systematic Limitations

Ferhan Baş Kaman(ANKARA YILDIRIM BEYAZIT UNIVERSITY)0000-0002-1879-9215
Ahmet Yücel(ANKARA YILDIRIM BEYAZIT UNIVERSITY)0000-0002-2364-9449
Sayfa
—
Yayın Tarihi
2025-12-29
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AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Validated CHAID models demonstrated high accuracy, underscoring the importance of comprehensive training and testing across diverse conditions to enhance effectiveness and safety.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The growing prominence of artificial intelligence has driven transformative innovations across sectors, with autonomous vehicles representing a salient manifestation of this technological shift.
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Temel Bulgular & Veri:A rigorous analysis of disengagement data is essential for evaluating the real-world performance and operational reliability of autonomous vehicles.
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Sonuç & Katkı:Validated CHAID models demonstrated high accuracy, underscoring the importance of comprehensive training and testing across diverse conditions to enhance effectiveness and safety.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2021–2023 Verileri
⚙️Değişkenler:Reliability · Autonomous Vehicles · Quantitative Decision-Making Methods

Abstract

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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Bu Makaleye Atıf Yap

Kaman, F. B., Yücel, A. (2025). Enhancing Autonomous Vehicle Safety Through Chaid Modeling: Influential Factors, Seasonal Variations, and Systematic Limitations. Sakarya University Journal of Computer and Information Sciences, Volume: 8 Issue: 4. https://doi.org/10.35377/saucis...1666618

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