Next Article in Journal
Design and Implementation of a Dual-Mass MEMS Gyroscope with High Shock Resistance
Previous Article in Journal
Real-Time Processing Library for Open-Source Hardware Biomedical Sensors
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(4), 1036; https://doi.org/10.3390/s18041036

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

1
University of Engineering and Technology, Vietnam National University in Hanoi (VNU-UET), Hanoi 123105, Vietnam
2
Academy of Journalism and Communication, Hanoi 123105, Vietnam
3
Posts and Telecommunications Institute of Technology in Hanoi (PTIT), Hanoi 151100, Vietnam
4
Information Technology Institute, Vietnam National University in Hanoi (VNU-ITI), Hanoi 123105, Vietnam
*
Authors to whom correspondence should be addressed.
Received: 18 January 2018 / Revised: 23 March 2018 / Accepted: 26 March 2018 / Published: 29 March 2018
(This article belongs to the Section Intelligent Sensors)
Full-Text   |   PDF [17593 KB, uploaded 3 May 2018]   |  

Abstract

In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers’ vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art. View Full-Text
Keywords: vehicle mode; driving event; smartphone sensor; motorbike assistance; optimized window size; optimized overlapping ratio vehicle mode; driving event; smartphone sensor; motorbike assistance; optimized window size; optimized overlapping ratio
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Lu, D.-N.; Nguyen, D.-N.; Nguyen, T.-H.; Nguyen, H.-N. Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones. Sensors 2018, 18, 1036.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top