Abstract
Contributions of measurements for detecting drowsy driving are determined by calculation parameters, which are directly related to the accuracy of drowsiness detection. The previous studies utilized the same Unified Calculation Parameters (UCPs) to compute each driver’s measurements. However, since each driver has unique driving behavior characteristics, namely, driver fingerprinting, Individual Drivers’ Best Calculation Parameters (IDBCPs) making measurements more discriminative for drowsiness are various. Regardless of the difference in driver fingerprinting among the drivers being tested, using UCPs instead of IDBCPs to compute measurements will limit the drowsiness-detection performance of the measurements and reduce drowsiness-detection accuracies at the individual driver level. Thus, this paper proposed a model to optimize calculation parameters of individual driver’s measurements and to extract individual driver’s measurements that effectively distinguish drowsy driving. Through real vehicle experiments, we collected naturalistic driving data and subjective drowsy levels evaluated by the Karolinska Sleepiness Scale. Eight nonintrusive drowsiness-related measurements were calculated by double-layer sliding time windows. In the proposed model, we firstly applied the Wilcoxon test to analyze differences between measurements of the awake state and drowsy state, and constructed the fitness function reflecting the relationship between the calculation parameters and measurement’s drowsiness-detection performance. Secondly, the genetic algorithms were used to optimize fitness functions to obtain measured IDBCPs. Finally, we selected measurements calculated by IDBCPs that can distinguish drowsy driving to constitute individual drivers’ optimal drowsiness-detection measurement set. To verify the advantages of IDBCPs, the measurements calculated by UCPs and IDBCPs were, respectively, used to build driver-specific drowsiness-detection models: DF_U and DF_I based on the Fisher discriminant algorithm. The mean drowsiness-detection accuracies of DF_U and DF_I were, respectively, 85.25% and 91.06%. It indicated that IDBCPs could enhance measurements’ drowsiness-detection performance and improve the drowsiness-detection accuracies. This paper contributed to the establishment of personalized drowsiness-detection models considering driver fingerprinting differences.
Highlights
Drowsy driving is a typically dangerous driving behavior, which seriously threatens traffic safety and causes substantial financial costs to individuals and society [1,2,3]
For a participant, the same original experimental data were used to calculate Unified Calculation Parameters (UCPs) and Individual Drivers’ Best Calculation Parameters (IDBCPs), so the experimental setting had no influence on the results. us, through iterating various UCPs and comparing the number of drivers whose drowsiness can be detected by measurements calculated by different UCPs, we found the suitable UCPs of each measurement which can make the number of participants whose P value < 0.05 maximum
All closed lines were not circular but irregular polygons. It meant that the distributions of SDLP calculated by the same calculation parameters of 6 participants were different, which was the outcome of driver fingerprinting differences in lane control behavior. ere were differences in the median of SDLP among participants in the awake state and drowsy state
Summary
Drowsy driving is a typically dangerous driving behavior, which seriously threatens traffic safety and causes substantial financial costs to individuals and society [1,2,3]. A review of previous studies indicated that there was an obvious association between drowsy driving and the risk of traffic accidents [4, 5]. E studies pointed out that 7% of all accidents and 16.5% of all road casualties were related to drowsy driving [6]. In the European Union, about 20% of commercial transport crashes were caused by drowsy drivers [7]. It was found that the risk of a near-crash event was significantly increased when drivers were drowsy after a night shift [8]. It is a problem to judge whether a traffic accident is caused by drowsiness [9]. Many accidents related to drowsiness have not been reported due to the lack of objective criteria for judging drowsiness occurrence, and the actual hazards of drowsiness driving may be more serious [10]
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