GELİŞMİŞ SÜRÜCÜ DESTEK SİSTEMLERİ İÇİN TRAFİK İŞARETLERİNE DUYARLI YAPAY ZEKÂ MODELİ

Öz

Günümüzde robotik ve otonom sistemlerin giderek yaygınlaşması, sürüş ortamının algılanması ve sürücülere ya da otonom sistemlere gerçek zamanlı bilgi sunan Gelişmiş Sürücü Destek Sistemleri (ADAS) ile bütünleştiğinde, yol güvenliği açısından büyük bir dönüşüm sağlamaktadır. Özellikle bilgisayar teknolojilerindeki ilerlemeler ve derin öğrenme tabanlı yöntemlerin başarısı, hem insan faktöründen kaynaklanan hataları en aza indirme hem de trafiği akıcı, güvenli ve konforlu hale getirme potansiyeli taşımaktadır. Bu çalışmada, otoyollardaki trafik akışı sorunlarını, zaman kaybını ve maliyet artışlarını azaltmak amacıyla trafik işaretlerine, özellikle de trafik ışıklarına duyarlı bir yapay zekâ modeli geliştirilmiştir. Bu bağlamda çalışmada, geleneksel otomatik olay tespit (AID) yöntemlerinde sıkça karşılaşılan düşük tespit oranları ve yüksek yanlış alarm sorunlarını aşmak üzere, sinir ağları ve derin öğrenme teknikleri kullanılmıştır. Özellikle geliştirilmiş YOLOv8 algoritması kullanılarak ileri seviye nesne tespiti algoritmaları yardımıyla, trafik ışıkları da dâhil olmak üzere çeşitli yol işaretleri ve şeritler, farklı çevresel koşullar ve görüş açıları altında yüksek doğrulukla saptanmıştır. Böylece yalnızca trafik akışına yönelik veriler değil, aynı zamanda işaretlerin anlamları, konumları ve yönleri de modele entegre edilerek, sürücülere ve otonom araçlara daha kapsamlı ve güncel bilgi ortamı sunulmuştur. Sonuç olarak geliştirilen modelin, trafik yönetim merkezlerinin operasyonel verimliliğini artırması, sürücü güvenliği ve konforunu iyileştirmesi ve otonom araçların sürekli değişen sürüş ortamlarında daha etkin kararlar alması sağlanmıştır.

Anahtar Kelimeler

• ADAS
• Otonom Araç
• Trafik işaretlerine duyarlı sistem.

A TRAFFIC SIGN-AWARE ARTIFICIAL INTELLIGENCE MODEL FOR ADVANCED DRIVER ASSISTANCE SYSTEMS

Öz

The increasing prevalence of robotics and autonomous systems today, when integrated with Advanced Driver Assistance Systems (ADAS) that perceive the driving environment and provide real-time information to drivers or autonomous systems, is bringing about a major transformation in terms of road safety. Specifically, advancements in computer technologies and the success of deep learning-based methods hold the potential to both minimize errors stemming from human factors and make traffic smooth, safe, and comfortable. In this study, an artificial intelligence model sensitive to traffic signs, especially traffic lights, has been developed to reduce traffic flow problems, time loss, and cost increases on highways. In this context, the study utilizes neural networks and deep learning techniques to overcome the low detection rates and high false alarm issues frequently encountered in traditional automatic incident detection (AID) methods. Advanced object detection algorithms, particularly with the enhanced YOLOv8 algorithm, have been used to accurately identify various road signs and lanes, including traffic lights, under different environmental conditions and viewing angles. Thus, not only traffic flow data but also the meanings, locations, and directions of signs were integrated into the model, providing drivers and autonomous vehicles with a more comprehensive and up-to-date information environment. As a result, the developed model is expected to increase the operational efficiency of traffic management centers, improve driver safety and comfort, and enable autonomous vehicles to make more effective decisions in constantly changing driving environments.

Anahtar Kelimeler

• ADAS
• Autonomous Vehicle
• Traffic Sign Recognition System.

Kaynakça

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