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使用眼动追踪技术理解自行车骑行者的视觉行为:一项系统综述。

Understanding Cyclists' Visual Behavior Using Eye-Tracking Technology: A Systematic Review.

作者信息

Kchour Fatima, Cafiso Salvatore, Pappalardo Giuseppina

机构信息

Department of Civil Engineering and Architecture, University of Catania, 64 Santa Sofia Street, 95123 Catania, Italy.

出版信息

Sensors (Basel). 2024 Dec 24;25(1):22. doi: 10.3390/s25010022.

Abstract

Eye-tracking technologies are emerging in research aiming to understand the visual behavior of cyclists to improve their safety. These technologies gather real-time information to reveal what the cyclists look at and how they respond at a specific location and time. This systematic review investigates the use of eye-tracking systems to improve cyclist safety. An extensive search of the SCOPUS and WoS databases, following the PRISMA 2020 guidelines, found 610 studies published between 2010 and 2024. After filtering these studies according to predefined inclusion and exclusion criteria, 25 were selected for final review. The included studies were conducted in real traffic or virtual environments aiming to assess visual attention, workload, or hazard perception. Studies focusing on other types of road users or participants not involved in active cycling were excluded. Results reveal the important impact of road elements' design, traffic density, and weather conditions on cyclists' gaze patterns. Significant visual workload is imposed mainly by intersections. Along with the valuable insights into cyclist safety, potential biases related to small sample sizes and technological limitations were identified. Recommendations for future research are discussed to address these challenges through more diverse samples, advanced technologies, and a greater focus on peripheral vision.

摘要

眼动追踪技术正在研究中兴起,旨在了解骑自行车者的视觉行为以提高他们的安全性。这些技术收集实时信息,以揭示骑自行车者在特定地点和时间看什么以及如何做出反应。本系统综述调查了眼动追踪系统在提高骑自行车者安全性方面的应用。按照PRISMA 2020指南对SCOPUS和WoS数据库进行广泛检索,发现2010年至2024年间发表了610项研究。根据预定义的纳入和排除标准对这些研究进行筛选后,选择了25项进行最终审查。纳入的研究在实际交通或虚拟环境中进行,旨在评估视觉注意力、工作量或危险感知。专注于其他类型道路使用者或未参与主动骑行的参与者的研究被排除。结果揭示了道路元素设计、交通密度和天气条件对骑自行车者注视模式的重要影响。显著的视觉工作量主要由十字路口造成。除了对骑自行车者安全的宝贵见解外,还发现了与小样本量和技术限制相关的潜在偏差。讨论了未来研究的建议,以通过更多样化的样本、先进技术以及对周边视觉的更多关注来应对这些挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d52b/11723293/285acc0caacc/sensors-25-00022-g001.jpg

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