Paper Info
Title
A Fused Method of Machine Learning and Dynamic Time Warping for Road Anomalies Detection
Abstract
To discover the condition of roads, a large number of detection algorithms have been proposed, most of which apply machine learning methods by time and frequency processing in acceleration and velocity data. However, few of them pay attention to the similarity of the data itself when the vehicle passes over the road anomalies. In this article, we propose a method to detect road anomalies by comparing the data windows with various length using Dynamic Time Warping(DTW) method. We propose a model to prove that the maximum acceleration of a vehicle passing through a road anomaly is linear with the height of the road barrier, and it’s verified by an experiment. This finding suggests that it is reasonable to divide the window by threshold detection. We also apply a brief random forest filter to roughly distinguish normal windows from anomaly windows using the aforementioned theory, in order to reduce the time consumption. From our study, a system is proposed that utilizes a series of acceleration data to discover where might be anomalies on the road, named as Quick Filter Based Dynamic Time Warping (QFB-DTW). We show that our method performs clearly beyond some existing methods. To support this conclusion, experiments are conducted based on three data sets and the results are statistically analyzed. We expect to lay the first step to some new thoughts to the field of road anomalies detection in subsequent work.
Year
DOI
Venue
2022
10.1109/TITS.2020.3016288
IEEE Transactions on Intelligent Transportation Systems
Keywords
DocType
Volume
Threshold detection,sliding window,accelerometer,dynamic time warping,machine learning
Journal
23
Issue
ISSN
Citations 
2
1524-9050
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Zengwei Zheng1277.99
Mingxuan Zhou261.76
Yuanyi Chen361.08
MeiMei Huo433.15
Lin Sun51459.46
Sha Zhao6489.96
dan chen7254.24