Paper Info
Title
CoDR: Correlation-based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data.
Abstract
A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduce data volume by considering the correlations among heterogeneous driving data. Among heterogeneous datasets, CoDR first chooses one set as a pivot data. Then, according to the objective of data collection, it identifies data ranges relevant to the objective from the pivot dataset. Finally, it investigates correlations among sets, and reduces data volume by eliminating irrelevant data from not only the pivot set but also other remaining datasets. CoDR gathers four heterogeneous driving datasets: two videos for front view and driver behavior, OBD-II and GPS data. We show that CoDR decreases data volume by up to 91%. We also present diverse analytical results that reveal the correlations among the four datasets, which can be exploited usefully for edge computing to reduce data volume on the spot.
Year
DOI
Venue
2020
10.3390/s20061677
SENSORS
Keywords
DocType
Volume
heterogeneous driving data,intelligent data analysis,data reduction,drowsiness detection,correlation,implementation
Journal
20
Issue
ISSN
Citations 
6
1424-8220
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Junho Park100.34
Yoojin Chung200.34
Jongmoo Choi38711.95