Paper Info
Title
Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics
Abstract
AbstractAbstractRoad roughness is a measure of how uncomfortable a ride is, and provides an important indicator for the needs of roadway maintenance or repavement, which is closely tied to the state and federal budget prioritization. As such, accurate and timely monitoring of deteriorating road conditions and following maintenance are essential to improve the overall ride quality on the road. Various technologies, including vehicle‐mounted laser profiling systems, have been developed and adopted for road roughness (e.g., IRI—International Roughness Index) measurement; however, their high cost limits their use. While recent advances in smartphone technologies allow us to use their embedded accelerometers for road roughness monitoring, the complicated process of necessary vehicle calibration hinders the widespread use of the technology in the actual practices. In this work, a deep learning IRI estimation method is proposed with the goal of using anonymous (i.e., calibration‐free) vehicles and their responses measured by smartphones as road roughness sensors. A state‐of‐the‐art deep learning algorithm (i.e., CNN—convolutional neural network) and multimetric vehicle dynamics data (i.e., accelerometer, gyroscope), possibly measured by drivers’ smartphones, are employed for the purpose. Optimized CNN architecture and data configuration have been investigated to achieve the best performance. The efficacy of the proposed method has been numerically validated using real road IRI information (i.e., Speedway, Tucson, AZ), real driving speed profiles, and four different types of vehicle data with associated uncertainties.
Year
DOI
Venue
2020
10.1111/mice.12546
Periodicals
DocType
Volume
Issue
Journal
35
11
ISSN
Citations 
PageRank 
1093-9687
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Jong-Hyun Jeong100.34
Hongki Jo200.34
Gregory Ditzler321416.55