Otonom Sürüşte Sürücü Konumundan Yolcu Konumuna Geçiş: Otonom Sürüş Kullanıcı Deneyiminde Bilişsel Yükün Nicel Olarak Ölçülmesine Yönelik VR Simülatör Tabanlı Bir Pilot Çalışma

Abstract

Otonom sürüş teknolojileri, sürücünün rolünü aktif kontrolcüden giderek pasifleşen bir denetleyiciye dönüştürmekte ve bu dönüşüm, kullanıcıların bilişsel süreçlerini doğrudan etkilemektedir. Laboratuvar tabanlı simülasyonlar ve gerçek araç yol testleri; ölçeklenebilirlik, sınırlı senaryo çeşitliliği ve katılımcılar üzerindeki değerlendirme baskısı gibi sınırlamalar nedeniyle bu deneyimsel dönüşümü bütüncül biçimde incelemede yetersiz kalabilmektedir. Bu çalışma, söz konusu sınırlamaları aşmak amacıyla sanal gerçeklik (VR), oyun simülatörü ve göz izleme teknolojilerini bir araya getiren bir kullanıcı deneyimi (UX) test ve veri analiz çerçevesi önermektedir. Sanal gerçeklik tabanlı bir oyun simülatörü ortamında, dört farklı kontrol seviyesinin (otomasyonsuz manuel sürüşten tam otonom yolcu konumuna kadar) ve değişen dört görev zorluk düzeyinin birleştirilmesiyle toplam on altı senaryo oluşturulmuştur. Önerilen çerçevenin teknik uygulanabilirliğini değerlendirmek amacıyla bu ortamda bir pilot çalışma yürütülmüştür. On altı senaryonun her birinde katılımcıların göz bebeği çapı ve bakış hareketi verileri, sanal gerçeklik tabanlı göz izleme sensörleri kullanılarak eşzamanlı olarak kaydedilmiştir. Göz bebeği çapındaki ve bakış hareketi örüntülerindeki değişimler nicel olarak analiz edilerek, her senaryo için bilişsel yükü temsil eden standartlaştırılmış bir Bilişsel Yük Skoruna (BYS) dönüştürülmüştür. BYS dönüşüm sürecini standartlaştırmak ve tekrarlanabilir hâle getirmek amacıyla CoLoX adlı Python tabanlı bir yazılım aracı geliştirilmiştir. Bu yazılım, çalışmada açıklanan analiz yöntemlerini izleyerek göz izleme sensörlerinden elde edilen verileri, farklı senaryolara ait bilişsel yük skorları üretmek için kullanmaktadır. Pilot çalışma sonuçları, önerilen yaklaşımın teknik olarak uygulanabilir olduğunu ve çok katmanlı biyometrik verilerin farklı sürüş koşullarında bilişsel yük göstergeleri elde etmek için kullanılabileceğini ortaya koymaktadır. Önerilen çerçeve, otonom sürüş deneyimini yalnızca teknik otomasyon metriklerine indirgemek yerine bilişsel yük odaklı bir kullanıcı deneyimi tasarım problemi olarak ele almak için ölçeklenebilir ve sürdürülebilir bir temel sunmaktadır. Bir pilot araştırma olarak bulgular, istatistiksel genellemeyi desteklemekten ziyade yöntemsel uygulanabilirliği göstermeyi amaçlamaktadır.

Keywords

• Kullanıcı Deneyimi Analizi
• Otonom Sürüş Deneyimi
• Bilişsel Yük Analizi
• Sanal Gerçeklik tabalı Göz İzleme Teknolojisi

The Transitions from Self-Driving to Passenger Mode: A VR Simulator-Based Pilot Study on Quantifying Cognitive Load in Autonomous Driving UX

Abstract

Autonomous driving technologies are transforming the driver’s role from an active controller into an increasingly passive supervisor, and this transformation directly affects users’ cognitive processes. Laboratory-based simulations and real-vehicle road tests may remain insufficient for examining this experiential transformation in an integrated manner, due to limitations including scalability, limited scenario diversity, and evaluation pressure on participants. This study proposes a user experience (UX) testing and data analysis framework that integrates virtual reality (VR), a game simulator, and eye-tracking technologies to overcome these limitations. Sixteen scenarios were constructed in a VR-based game simulator environment by combining four levels of control, ranging from no automation to fully autonomous passenger-mode driving, with four increasing levels of task difficulty. A pilot study was conducted in this environment to assess the technical feasibility of the proposed framework. In each of the sixteen scenarios, participants’ pupil diameter and eye gaze movement data were synchronously recorded using virtual reality–based eye-tracking sensors. Variations in pupil diameter and gaze-movement patterns were quantitatively analyzed and converted into a standardized Cognitive Load Score (CLS) to represent cognitive load in each scenario. A Python software tool called CoLoX was developed to standardize the CLS transformation process and make it reproducible. It uses data from eye-tracking sensors, following the analysis methods outlined in this study, to generate cognitive load scores across different scenarios. Results from the pilot study indicate that this approach is technically feasible and that multi-layered biometric data can be used to derive cognitive load indicators across varying driving conditions. The proposed framework provides a scalable and lasting foundation for studying the autonomous driving experience as a user experience design challenge, focusing on cognitive load rather than solely on technical automation metrics. As a pilot investigation, the findings are intended to demonstrate methodological applicability rather than to support statistical generalization.

Keywords

• User Experience Analysis
• Autonomous Driving Experience
• Cognitive Load Analysis
• VR Based Eye-Tracking

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