Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network

Abstract

The driver is one of the most important factors in road traffic. Monitoring the driver’s driving status can greatly improve the safety and road operation efficiency of urban road traffic in the case of multiple traffic modes. Fatigue has a significant impact on drivers’ safety on the road, particularly while driving in a monotonous environment for a long time. In this study, the eye movement parameters of 36 drivers were collected through the simulation experiment of a driving simulator. The pupil area and percentage of eye closure (PERCLOS) in driving scenes of the expressway and low-grade rural road were combined with the Stanford Sleepiness Scale (SSS) to determine the threshold of fatigue degree in different monotonous driving scenarios. A recognition model of different fatigue degrees of drivers is built based on the deep learning method of a long short-term memory network (LSTM) to detect the varied fatigue degrees of drivers. The result shows that the fatigue degree of drivers increases as driving time increases on both expressways and low-grade rural roads. In the same driving time, the driver felt tired faster on the expressway, and the fatigue degree was significantly higher than that on the country road. The recognition rate of the established fatigue degree recognition model for driver’s awake state, mild fatigue, moderate fatigue, and severe fatigue is 100%, 93.1%, 98.4%, and 100% respectively, and the total recognition rate can reach 97.8%, which is higher than the recognition accuracy of the traditional machine learning approach.

Regression Analysis of Heart Rate for Driving Fatigue using Box-Behnken Design

“…A study revealed that a driver's alertness decreased significantly when driving in a less demanding environment and route design [30]. A different study confirmed that the driver becomes fatigued quickly while driving on the expressway and that the fatigue level is significantly higher than when driving on a road with varying geometry for the same duration [31]. Under such conditions, drivers tend to experience more passive fatigue symptoms, which can lead to fatigued driving.…”

Scented Solutions: Examining the Efficacy of Scent Interventions in Mitigating Driving Fatigue

“…We recognize the complexity of factors that influence fatigued driving. Studies have highlighted the impact of various factors such as driver age, gender [ 15 ], psychological factors [ 42 ], driving experience [ 43 ], sleep status [ 44 ], work hours [ 2 ], and environment [ 18 ] on fatigued driving. Studies have indicated that olfactory abilities differ by gender [ 45 , 46 ].…”

“…The laboratory maintained a temperature range of 15~25 • C. The road traffic environment was deliberately set to be monotonous, featuring a sunny day with vehicles traveling on a motorway bordered by trees on both sides. Subjects were instructed to drive at a speed of 80-100 km/h without overtaking, as monotony in the driving environment is known to heighten the susceptibility to fatigue [18].…”

Prediction and Comparison of In-Vehicle CO2 Concentration Based on ARIMA and LSTM Models

“…A study conducted by the American Automobile Association revealed that fatigued drivers were responsible for three-and-a-half times more crashes than that reported in news accounts from 1999-2008 [24]. A report released by the National Highway Traffic Safety Administration (NHTSA) indicated that at least 83,000 vehicle accidents were caused by driver fatigue and drowsiness; these accidents led to 37,000 injuries and 900 fatalities [25].…”