A new computerized driving system using cognitive skills approach in minimizing driving fatigue among young drivers - Ibrahim, Muhammad Shafiq (2024)
Driving fatigue is a leading factor in traffic accidents among young drivers in many countries, including Malaysia. Despite the availability of many fatigue detection technologies and the development of decision support systems to meet transportation industry concerns, the number of traffic accidents in Malaysia is increasing. Hence, the current research aimed to develop a computerized system for driving fatigue that employed cognitive skill analysis to predict the level of fatigue experienced by young drivers. This system would warn about the driver’s current situation and propose a solution to determine if it is safe to continue driving. Five major phases involved as the pillar in the development of the system: phase 1 (knowledge acquisition), phase 2 (experimental design), phase 3 (perform real world driving experiment), phase 4 (perform regression analysis) and phase 5 (develop decision support system for driving fatigue). The system assessed driving fatigue through the relationship between factors, namely driving duration, body mass index (BMI), types of roads and gender, and cognitive skills, such as working memory capacity, attention level and decision-making skills. These cognitive skills were assessed using an electroencephalogram (EEG) through the analysis of theta (θ), alpha (α) and beta (β)-waves. A total of 52 real-road experimental runs were conducted by 52 subjects. The DSSfDF’s functional framework was divided into three parts. First, the system predicted the user’s power spectral density (PSD) data during driving for θ-waves (working memory capacity), α-waves (attention level) and β-waves (decision-making skills) utilising the 12 equations by entering information, including BMI, gender and types of roads in the system using the Graphical User Interface (GUI). A timer button (which represents driving duration) was then clicked, and the driving began. The system then started to calculate the user’s PSD data, starting at 00.00.01 seconds and onwards. Second, at minute 30 of the drive, the first alarm, accompanied by the warning ‘Stay Alert’, was activated for all users. Third, the final alarm accompanied by the warning ‘Stop Driving and Have a Rest’ was activated based on the user’s current PSD data and the PSD values as a person fatigued obtained by a previous study. The Prob>F values for factors A (driving duration), B (BMI), C (types of roads), and D (gender) for all three cognitive skills were all less than 0.05, indicating that these factors had a significant influence on cognitive skills. The diagnostic plots showed that all 12 equations accurately predicted the experimental data compared to the actual data. The DSSfDF validation experiments revealed that all drivers self-reported experiencing severed fatigue when the final warning was triggered. This study suggested that driving fatigue was present at the end of the driving session and that the final warning triggered by the DSSfDF was compatible with drivers’ current fatigue level while driving. Therefore, the current study has accomplished its goal of addressing the problem of driving fatigue among young drivers. The findings of the current study could provide valuable insights for researchers and decision-makers involved in road safety to mitigate the occurrences of traffic accidents caused by driver fatigue.