Steering behavior-based fatigue detection: Evaluation and implementation for drowsy driver warning system

Abstract

This research was conducted to analyze steering behavior as an alternative approach to detecting driver drowsiness. The thesis examined standard metrics such as steering wheel angle, steering wheel angle rate, yaw rate, and lane position—variables directly and indirectly related to steering behavior—to assess how much information they bear about drowsiness. A derived metric, the steering reversal rate, was also analyzed to further explain the effects of drowsiness. These metrics demonstrated a strong correlation with driver drowsiness, which was subjectively measured using the Karolinska Sleepiness Scale (KSS). Based on this analysis, two parameters derived from the steering reversal rate—micro and macro corrections—were used to develop two methods for detecting drowsiness. These parameters were significant because, as a driver becomes drowsier, the frequency of micro-corrections tends to decrease, while macro-corrections increase. Two methods have been developed to detect a drowsy driver based on the above analysis. The first method employed a logistic regression model, using the absolute values of micro and macro corrections to directly correlate with the KSS ratings. This approach did not account for temporal patterns and treated the data independently of its time-series nature. In contrast, the second method incorporated a time-series perspective by evaluating changes in micro and macro correction rates over time rather than relying on their absolute values. During development, it was observed that vehicle speed significantly influenced steering behavior. At lower speeds, even non-drowsy drivers exhibited more macro corrections and fewer microcorrections. However, at speeds above 65 km/h, non-drowsy drivers typically made more micro-corrections and fewer macro-corrections. This insight enhanced the robustness of the second method, wherein vehicle speed was considered one of the contributing factors in analyzing driver steering behavior. Additionally, the second method involved a learning phase for each individual driver, allowing for personalized threshold values. This driver-specific calibration improved adaptability. Overall, the second method of real-time analysis of changes in micro and macro correction rates proved to be a more effective and reliable approach, yielding better results than the current system(based on lane distance) for detecting driver drowsiness.