Safe Deep Driving Behavior Detection (S3D)

Abstract

The human factor is one of the most critical parameters in car accidents and even traffic occurrences. Driving style affected by human factors comprises driving events (maneuvers) and driver behaviors. Driving event detection is the fundamental step of identifying driving style and facilitates predicting potentially unsafe behaviors, preventing accidents, and imposing restrictions on high-risk drivers. This paper proposes a deep hybrid model to detect safe driver behaviors and driving events using real-time smartphone sensor signals. The ensemble of Multi-layer Perceptron, Support-Vector Machine, and Convolutional Neural Network classifiers process each driving event sample. In order to evaluate our model, we develop an Android Application to capture smartphone sensor signal data. We capture about 24000 driving data from 50 drivers. Results indicate that the fusion model performs better than each individual classifier in terms of Accuracy, False Positive Rate (FPR), and Specificity (96.75, 0.004, and 0.996). This research gives insights to Auto-mobile developers to focus on the speed and cost efficiency of smartphone driver monitoring platforms. Although some insurance and freight management companies utilize smartphones as their monitoring platforms, the market share of these use cases is meager and could improve rapidly with the promotion of new smartphones with better processing and storage.