E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Abstract

The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase in-car awareness for improved safety. This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with real-time recommendations. The highest performing E2DR variant, which included the ResNet50 and VGG16 models, achieved a test accuracy of 92% as applied to state-of-the-art datasets, including the State Farm Distracted Drivers dataset, using novel data splitting strategies.

A Novel Approach to Detect Driver Drowsiness Using Transfer Learning and Hybrid Features

“…InceptionV3 [29], VGG16 [30,40], ResNet50 [31,32], DenseNet201 [33] and MobileNetV2 [33,34] models are chosen for transfer learning to figure out drowsiness recognition process. Chosen these models because they perform well in computer vision.…”

In-Vehicle Sensing for Smart Cars

“…Instead of using either the big motion [136]- [139] or minor motion [140]- [145], [229]- [231], the second category [140], [146], [146], [206], [216]- [223], [232]- [234] fuses features from the big motion and minor motion together as shown in Fig. 3 and Table XI.…”

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators

“…The aforementioned distractions can result in injuries, property damage, and sometimes fatalities. Numerous efforts have been taken to detect driver distractions promptly to develop a reliable system to support the drivers accordingly [8,9]. Prominent techniques to detect driver distractions are monitoring the driver's behavior using a camera and image processing technique or monitoring the brain's activity using an electroencephalogram (EEG) [10][11][12][13][14][15].…”