Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded Sensors Using an Optimized Stacked-LSTM Neural Networks

Abstract

Driving behavior classification is an essential real-world requirement in different contexts. In traffic safety, avoiding traffic accidents by taking corrective actions against aggressive behaviors is necessary to protect drivers. Similarly, in the automotive insurance industry, distinguishing between driving behaviors is essential to adopt usage-based insurance (UBI) policies. Also, in the ridesharing industry, monitoring and evaluating driving behaviors is critical for risk assessment and service improvement. This research presents a deep learning-based solution for driving behavior classification using an optimized Stacked-LSTM model based on the signals of smartphone embedded sensors generating two different classification models: three-class and binary. Three-class classification distinguishes between normal, drowsy, and aggressive behaviors to support advanced driver-assistance systems (ADAS). Binary classification differentiates between aggressive and non-aggressive behaviors to support commercial applications, such as ridesharing services and automotive insurance services based on UBI. Our time-series classification models have been evaluated on the public UAH-DriveSet dataset. Using the proper number and type of features, the optimum factor of upsampling for the raw signals, and the optimum time-series window size, our proposed Stacked-LSTM model made a breakthrough in the F1-score when applied to the aforementioned dataset. The achieved scores are 99.49% and 99.34% for the Three-class and binary classification models, respectively. Comparisons with state-of-the-art models, our three-class classification model surpassed the highest published F1-score of 91% by 8.49% when applied to the aforementioned dataset.