Driving Distraction Recognition Based on Probability Distribution Evolution Characteristics of Driving Behaviors

Abstract:

Risky driving behaviors are the main cause of road traffic accidents， with a third of accidents caused by distracted driving. Driving distraction recognition is an efficient approach to improve traffic safety. Current methodologies for driving distraction recognition mainly rely on aggregated multi-sensor data， which limits their extensive application to existing vehicles. Therefore， a two-stage method is proposed in this paper based on inertial measurement unit （IMU） data， a widely available data， for driving distraction recognition. In the first stage， a characterization method based on the evolution of probability density distribution is proposed to represent distracted driving behaviors that are closely coupled with operating conditions. In the second stage， the deep forest algorithm is employed to construct a classification model capable of recognizing driving distraction in complex practical scenarios. An empirical experiment is conducted using IMU data from smartphones in online hailing cars in Shanghai to validate the proposed recognition method. The results indicate that： the distraction recognition method proposed is validated， and the longitudinal characteristics represent the distracted driving behaviors. The proposed characteristics， when compared with the traditional ones， significantly enhance the performance of the model with an increase of 20.4 % in accuracy and 10.2 % in precision. The deep forest model reduces false alarms by more than 10 % while maintaining a high recall rate， compared to support vector machine （SVM） and extreme gradient boosting （XGBoost）.