Scipediacontent: Scipediacontent moved page Draft Content 143170576 to 238,295ow

2021-03-23T02:48:07Z

Scipediacontent moved page Draft Content 143170576 to 238,295ow

Scipediacontent: Created page with " == Abstract == A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driv..."

2021-03-23T02:45:58Z

Created page with " == Abstract == A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driv..."

New page

== 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.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_143170576-beopen1413-8251-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.3390/s20061677 http://dx.doi.org/10.3390/s20061677] under the license https://creativecommons.org/licenses/by

* [http://europepmc.org/articles/PMC7146115 http://europepmc.org/articles/PMC7146115] under the license http://creativecommons.org/licenses/by/3.0/

* [https://www.mdpi.com/1424-8220/20/6/1677/pdf https://www.mdpi.com/1424-8220/20/6/1677/pdf]

* [https://dblp.uni-trier.de/db/journals/sensors/sensors20.html#ParkCC20 https://dblp.uni-trier.de/db/journals/sensors/sensors20.html#ParkCC20],
: [https://www.mdpi.com/1424-8220/20/6/1677/pdf https://www.mdpi.com/1424-8220/20/6/1677/pdf],
: [https://www.mdpi.com/1424-8220/20/6/1677 https://www.mdpi.com/1424-8220/20/6/1677],
: [https://www.ncbi.nlm.nih.gov/pubmed/32192221 https://www.ncbi.nlm.nih.gov/pubmed/32192221],
: [https://academic.microsoft.com/#/detail/3011279974 https://academic.microsoft.com/#/detail/3011279974] under the license cc-by

* [https://www.mdpi.com/1424-8220/20/6/1677 https://www.mdpi.com/1424-8220/20/6/1677],
: [https://doaj.org/toc/1424-8220 https://doaj.org/toc/1424-8220]

* [https://www.mdpi.com/1424-8220/20/6/1677/pdf https://www.mdpi.com/1424-8220/20/6/1677/pdf],
: [http://dx.doi.org/10.3390/s20061677 http://dx.doi.org/10.3390/s20061677]

under the license https://creativecommons.org/licenses/by/4.0/

← Older revision	Revision as of 02:48, 23 March 2021
(No difference)

238,295ow - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 143170576 to 238,295ow

Scipediacontent: Created page with " == Abstract == A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driv..."