Drive now, text later: Nonintrusive texting-while-driving detection using smartphones

Xuefeng Liu, Jiannong Cao, Shaojie Tang, Zongjian He, Jiaqi Wen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Texting-while-driving (T&D) is one of the top dangerous behaviors for drivers. Many interesting systems and mobile phone applications have been designed to help to detect or combat T&D. However, for a T&D detection system to be practical, a key property is its capability to distinguish driver's mobile phone from passengers'. Existing solutions to this problem generally rely on the user's manual input, or utilize specific localization devices to determine whether a mobile phone is at the driver's location. In this paper, we propose a method which is able to detect T&D automatically without using any extra devices. The idea is very simple: when a user is composing messages, the smartphone embedded sensors (i.e., gyroscopes, accelerometers, and GPS) collect the associated information such as touchstrokes, holding orientation and vehicle speed. This information will then be analyzed to see whether there exists some specific T&D patterns. Extensive experiments have been conducted by different persons and in different driving scenarios. The results show that our approach can achieve a good detection accuracy with low false positive rate. Besides being infrastructure-free and with high accuracy, the method does not access the content of messages and therefore is privacy-preserving.

Original languageEnglish
Article number7434641
Pages (from-to)73-86
Number of pages14
JournalIEEE Transactions on Mobile Computing
Volume16
Issue number1
DOIs
Publication statusPublished - 1 Jan 2017

Keywords

  • Data-driven pattern recognition
  • Drive
  • Mobile phone applications
  • Text

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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