Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference System

Abstract

Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrance by leveraging built-in smartphone sensors only. The results of our comprehensive evaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1% +/- 94.0% accuracy, outperforming other methods by 26.9% +/- 38.4% and maintains at least 87.8% accuracy regardless of smartphone positions and vehicle types.