Integration of Ensemble and Evolutionary Machine Learning Algorithms for Monitoring Diver Behavior Using Physiological Signals

The level of consciousness and the concentration of drivers while driving play a vital role for reducing the number of accidents. In recent decade, in-vehicle infotainment (IVI) [or in-car entertainment (ICE)] is one of the main reasons that lead to degradation of drivers performance and losing awareness. However, the impacts of some other reasons, such as drowsiness and driving fatigue, are entirely important as well. Hence, early detection of such performance degradation using different methods is a very hot research domain. To this end, the data set is collected using two different simulated driving scenarios: normal and loaded drive (17 elderly and 51 young/35 male and 33 female). This paper, therefore, concentrates on driving performance analysis using various machine learning techniques. The optimization part of the proposed methodology has two main steps. In the first step, the performances of the K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB) algorithms are improved using bagging, boosting, and voting ensemble learning techniques. Afterward, four well-known evolutionary optimization algorithms [the ant lion optimizer (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and grey wolf optimizer (GWO)] are applied to the system for optimizing the parameters and as a result enhance the performance of whole system. The GWO-voting approach has the best performance compared to other hybrid methods with the accuracy of 97.50%. The obtained outcomes showed that the proposed system can remarkably raise the performance of the classical algorithms used.