A Privacy-Preserving Learning Method for Analyzing HEV Driver's Driving Behaviors

The driving behaviors of electric vehicle (EV) and hybrid electric vehicle (HEV) drivers have received considerable attention in the literature. The use of image recognition in combination with GPS and driving data has emerged as a popular approach to improving driver safety. However, such methods often generate sensitive personal information, including driver images, names, and GPS locations, which may risk the safety of the driver's privacy. To address this issue, a privacy-preserving approach for identifying driver behavior characteristics is necessary. To achieve this, we utilize on-board diagnostic (OBD) interface vehicle-mounted devices to collect and analyze data from an electronic control unit (ECU), thereby collecting only onboard data for data processing and analysis. In this work, we propose deep learning models such as long short-term memory (LSTM) networks and gated recurrent units (GRUs), which enable the learning of specific behavior patterns and represent the state-of-the-art model to classify and predict driving behavior that potentially leads to dangerous accidents. The predictions were then converted into alarm signals and transmitted to the dashboard of vehicle. Our experiments showed that our proposed system model achieved excellent results with an excellent kappa score of 96.5%, demonstrating that it can accurately identify unique driving behaviors in a privacy-preserving manner.

Automated summary

The driving behaviors of electric vehicle and hybrid electric vehicle drivers have received attention in the literature. Image recognition combined with GPS and driving data is a popular approach to improving driver safety. However, this method generates sensitive personal information, risking driver's privacy. To address this, a privacy-preserving approach is necessary. In this work, deep learning models like LSTM and GRUs are proposed to classify and predict driving behavior accurately while protecting privacy.