A Machine Learning Approach to Road Surface Anomaly Assessment Using Smartphone Sensors

Road surface quality is essential for improving driving experience and reducing traffic accidents. Traditional road condition monitoring systems are limited in their temporal (speed) and spatial (coverage) responses needed for maintaining overall road quality. Several alternative systems have been p...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal 2020-03, Vol.20 (5), p.2635-2647
Main Authors: Basavaraju, Akanksh, Du, Jing, Zhou, Fujie, Ji, Jim
Format: Article
Language:English
Subjects:
accelerometer
Accelerometers
Artificial neural networks
Axes (reference lines)
Classification
Condition monitoring
crack
Cracks
decision tree
Driving
Feature extraction
Inspection
Machine learning
Manuals
multilayer perceptron
neural network
Neural networks
pavement condition
pothole
road condition
Road surface
Roads
Roads & highways
Sensors
smartphone sensor
Smartphones
Support vector machines
Surface properties
Traffic accidents
Vibrations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Road surface quality is essential for improving driving experience and reducing traffic accidents. Traditional road condition monitoring systems are limited in their temporal (speed) and spatial (coverage) responses needed for maintaining overall road quality. Several alternative systems have been proposed that utilize sensors mounted on vehicles. In particular, with the ubiquitous use of smartphones for navigation, smartphone-based road condition assessment has emerged as a promising new approach. In this paper, we propose to analyze different multiclass supervised machine learning techniques to effectively classify road surface conditions using accelerometer, gyroscope and GPS data collected from smartphones. Our work focuses on classification of three main class labels- smooth road, potholes, and deep transverse cracks. We hypothesize that using features from all three axes of the sensors provides more accurate results as compared to using features from only one axis. We also investigate the performance of deep neural networks to classify road conditions with and without explicit manual feature extraction. Our results indicate that models trained with features from all axes of the smartphone sensors outperform models that use only one axis. We also observe that the use of neural networks provides a significantly improved data classification. The machine learning approach discussed here can be implemented on a larger scale to monitor roads for defects that present a safety risk to commuters as well as to provide maintenance information to relevant authorities.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2952857