نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشگاه بین المللی امام خمینی، قزوین، ایران
2 دانشگاه بین المللی امام خمینی(ره)
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Objective: Driver fatigue is one of the most prevalent causes of road crashes. It affects physiological properties such as heart rate, breath rate, and EEG features, as well as other indicators like driver gestures and vehicle manoeuvres. This paper aims to investigate various driver fatigue detection and processing techniques and provide future insights.
Method: This study conducted a comprehensive literature review to explore the current methods and suggest future directions for driver fatigue detection and processing.
Results: The results indicate that physiological properties have the strongest correlation with driver drowsiness, making them more precise than other methods. Additionally, machine learning-based methods demonstrated superiority over other techniques.
Conclusion: The findings suggest that future approaches should utilize fusion-based machine learning methods to report and analyze driver drowsiness. Data fusion can involve integrating physiological signals, driver behavior, and driver status. Combining various physiological properties such as EEG, heart rate, and breath rate can achieve the highest accuracy due to multiple validations. This fusion approach is expected to provide more reliable and precise detection and analysis of driver fatigue.
کلیدواژهها [English]
)