Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

Abstract

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level driver tiredness diagnostic database based on physiological signals including ECG, EEG, EMG, and respiratory effort was developed for this aim. The EEG signal was used for processing and other recorded signals were used to confirm the driver’s fatigue so that fatigue was not confirmed based on self-report questionnaires. A customized architecture based on adversarial generative networks and convolutional neural networks (end-to-end) was utilized to select/extract features and classify different levels of fatigue. In the customized architecture, with the objective of eliminating uncertainty, type 2 fuzzy sets were used instead of activation functions such as Relu and Leaky Relu, and the performance of each was investigated. The final accuracy obtained in the three scenarios considered, two-level, three-level, and five-level, were 96.8%, 95.1%, and 89.1%, respectively. Given the suggested model’s optimal performance, which can identify five various levels of driver fatigue with high accuracy, it can be employed in practical applications of driver fatigue to warn drivers.

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

“…The limitation of this study can be considered in the number of binary states for classification. Peivandi et al [ 20 ] used physiological signals to automatically detect driver fatigue. In their research, these researchers used EEG, EMG, ECG, and EOG signals to automatically detect driver fatigue.…”

Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios

“…This is not only related to preserving lives, but also closely related to social stability and economic development. Through in-depth research and effective safety management, we are expected to reduce the occurrence of expressway traffic accidents and ultimately make a positive contribution to building a safe and efficient transportation system [4]. On the expressway, the overall speed of vehicles is usually high, and if the gap between the vehicle speed and the surrounding vehicle speed is too large, it may have a certain impact on the driving of the surrounding vehicles, which may lead to traffic accidents [5].…”