Drive Now Text Later Nonintrusive Texting-While-Driving Detection Using Smartphones

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 user’s manual input, or utilize specific localization devices to determine whether a mobile phone is at drive’s location. In this project a method was proposed 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.

Existing System

In the existing system, utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure approach is flexible with different turn sizes and driving speeds. Extensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving environments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over 90% with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., 95%) with a lower false positive rate.

Proposed System

In the proposed system the T&D patterns are identified using data collected from smartphone embedded sensors including gyroscopes, accelerometers and the GPS receiver. To obtain the content of the messages by hacking into the operating system to avoid privacy concerns of users. This T&D detection system works as an application installed in the smartphone users. Note that we only detect T&D but does not try to prohibit it. The results can be stored in the smartphone for later review or can be sent automatically to designated recipients. To provide information for usage-based insurance (UBI). Different from traditional insurance whereby the costs of motor insurance are dependent on history of claims, the UBI attempts to reward safe drivers by their present pattern of driving behaviour. Examples of UBI include Snapshot and Drive Wise. The proposed T&D detection app can be installed into mobile phones of drivers who are willing to buy UBI. The results about whether T&D is detected will be automatically sent to the corresponding UBI Company. To provide information for other apps which discourage T&D. For example, the Rode the winner of AT&T hackathon to promote its ‘don’t text while driving’ campaign in 2012, is an application that allows friends and family to organize themselves into a pack. A GPS tracks the location of each person in the pack at all times and alerts users whenever someone in the pack is texting and driving at the same time. Our T&D detection method can serves as the significant improvement on the Rode Dog by differentiating a driver from a passenger.

Architecture

ARCHITECTURE DIAGRAM