Detection and prediction of driver drowsiness using artificial neural network models

Abstract

Not just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsiness level will be reached. Moreover, we explore whether adding data such as driving time and participant information improves the accuracy of detection and prediction of drowsiness. Twenty-one participants drove a car simulator for 110min under conditions optimized to induce drowsiness. We measured physiological and behavioral indicators such as heart rate and variability, respiration rate, head and eyelid movements (blink duration, frequency and PERCLOS) and recorded driving behavior such as time-to-lane-crossing, speed, steering wheel angle, position on the lane. Different combinations of this information were tested against the real state of the driver, namely the ground truth, as defined from video recordings via the Trained Observer Rating. Two models using artificial neural networks were developed, one to detect the degree of drowsiness every minute, and the other to predict every minute the time required to reach a particular drowsiness level (moderately drowsy). The best performance in both detection and prediction is obtained with behavioral indicators and additional information. The model can detect the drowsiness level with a mean square error of 0.22 and can predict when a given drowsiness level will be reached with a mean square error of 4.18min. This study shows that, on a controlled and very monotonous environment conducive to drowsiness in a driving simulator, the dynamics of driver impairment can be predicted.

Driving Fatigue Prediction Model considering Schedule and Circadian Rhythm

“…As for driving performance indicators, because of the limited funds and lack of equipment, only the SDLP was analyzed. In addition, the collection frequency of KSS is a little high (one point every 5 minutes), and this may help drivers keep awake [54]. What is more, in these field experiments, participants need driving on the highway for at least 4 hours, and it may become difficult for drivers to judge their drowsiness [55] after a period of driving.…”

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

“…The reason for the popularity of ANNs is that is they are non-parametric statistical models, which do not need any assumptions between input and output variables [22], and that they have the ability to learn from experience and enhance their functions to improve classification and prediction accuracy [23]. Nonlinear modeling machine learning (such as ANNs) is used to extract information from noisy data, and can avoid over-fitting, making it generally more robust [24,25]. The ANNs are composed of nodes connected by directed links, and each link has a numeric weight [26].…”

Intelli-Car with Auto-Pilot for Accident Prevention

“…If this problem is not addressed, the car stops in the middle of the road and may result in an accident. Pertaining to drowsiness detection, there are many proposed methods such as based on physiological features as in [3], where eye blinking and head movements are considered ; by Image Processing as in [4], where images are analysed using neural networks; based on the driving pattern of driver as in [5], where pattern of pedal controlling based on the outside view is observed; based on biological features as in [6], where variations in heart rate are monitored; based on Blood Alcohol Concentration (BAC) as in [7], where driver"s perspiration is analysed for BAC, as in [1] and [2], where alcohol in breath is analysed using a MQ-3 sensor. In [7], the authors propose to mount the infrared sensors and heaters on steering wheel.…”