System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures

Abstract

The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

“…Amidei et al [52] have implemented a machine learning model where the data is derived from wrist device for acquiring skin conductance signal followed by using ensembled learning algorithm. The work carried out by Bajaj et al [53] have presented a model integrating facial features with skin response data where further CNN has been used for identifying facial features associated with driver's drowsiness. Similar trend of considering two different signals were also witnessed in study of Esteves et al [54] where facial features and ECG is used for facilitating continuous learning towards predictive analysis of driver's drowsiness.…”

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

“…The research study [12] proposes a method to detect drowsiness by integrating both non-intrusive and intrusive approaches. The experiments utilize the NTHU-DDD dataset, which includes data from 36 individuals displaying various behaviors indicative of drowsiness, such as yawning, slow blinking, dozing off, and the wearing of glasses or sunglasses under varied lighting conditions both during the day and at night.…”

“…Proposed Technique Performance Score [8] CNN 97% [12] Hybrid model 91% [13] ML model 80% [14] Bagging and boosting 89% [15] ML model 85% [31] YOLO network 73% [32] Yolo V3 98% [33] YOLO network 87% Our Novel VGLG 99%…”

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

“…Study [28] utilized photoplethysmography imaging (PPGI) to derive heart rate variability (HRV) and LF/HF ratio, achieving 92.5% accuracy by correlating these HRV-derived parameters with PERCLOS measurements. Moreover, a couple of studies [32,33] integrated PERCLOS with vehicle-based signals, such as steering wheel movement [32] and lane position [33], while another [34] merged PERCLOS with a galvanic skin response (GSR) sensor using Multi-Task Cascaded Convolutional Neural Networks (MTCNNs), effectively predicting the driver's transition from an awake to a drowsy state at 91% efficacy.…”