Wood et al 2014a - Revision history

Scipediacontent at 15:36, 25 January 2021

2021-01-25T15:36:24Z

Scipediacontent at 10:20, 22 January 2021

2021-01-22T10:20:03Z

Scipediacontent: Scipediacontent moved page Draft Content 875331376 to Wood et al 2014a

2020-10-26T09:18:41Z

Scipediacontent moved page Draft Content 875331376 to Wood et al 2014a

Scipediacontent: Created page with " == Abstract == Accurate and reliable global positioning system (GPS)-based vehicle use data are highly valuable for many transportation, analysis, and automotive considerati..."

2020-10-26T09:18:39Z

Created page with " == Abstract == Accurate and reliable global positioning system (GPS)-based vehicle use data are highly valuable for many transportation, analysis, and automotive considerati..."

New page

== Abstract ==

Accurate and reliable global positioning system (GPS)-based vehicle use data are highly valuable for many transportation, analysis, and automotive considerations. Model-based design, real-world fuel economy analysis, and the growing field of autonomous and connected technologies (including predictive powertrain control and self-driving cars) all have a vested interest in high-fidelity estimation of powertrain loads and vehicle usage profiles. Unfortunately, road grade can be a difficult property to extract from GPS data with consistency. In this report, we present a methodology for appending high-resolution elevation data to GPS speed traces via a static digital elevation model. Anomalous data points in the digital elevation model are addressed during a filtration/smoothing routine, resulting in an elevation profile that can be used to calculate road grade. This process is evaluated against a large, commercially available height/slope dataset from the Navteq/Nokia/HERE Advanced Driver Assistance Systems product. Results will show good agreement with the Advanced Driver Assistance Systems data in the ability to estimate road grade between any two consecutive points in the contiguous United States.

Document type: Report

== Full document ==
<pdf>Media:Draft_Content_875331376-beopen157-6071-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.2172/1134504 http://dx.doi.org/10.2172/1134504]

* [http://pdfs.semanticscholar.org/d893/d1b45dac01ed664a6651930e3c8fefc496f3.pdf http://pdfs.semanticscholar.org/d893/d1b45dac01ed664a6651930e3c8fefc496f3.pdf]

* [http://www.nrel.gov/docs/fy14osti/61109.pdf http://www.nrel.gov/docs/fy14osti/61109.pdf],[https://academic.microsoft.com/#/detail/2290841126 https://academic.microsoft.com/#/detail/2290841126]

← Older revision		Revision as of 15:36, 25 January 2021
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	== Abstract ==		== Abstract ==

−	~~Accurate~~ and reliable global positioning system (GPS)-based vehicle use data are highly valuable for many transportation, analysis, and automotive considerations. Model-based design, real-world fuel economy analysis, and the growing field of autonomous and connected technologies (including predictive powertrain control and self-driving cars) all have a vested interest in high-fidelity estimation of powertrain loads and vehicle usage profiles. Unfortunately, road grade can be a difficult property to extract from GPS data with consistency. In this report, we present a methodology for appending high-resolution elevation data to GPS speed traces via a static digital elevation model. Anomalous data points in the digital elevation model are addressed during a filtration/smoothing routine, resulting in an elevation profile that can be used to calculate road grade. This process is evaluated against a large, commercially available height/slope dataset from the Navteq/Nokia/HERE Advanced Driver Assistance Systems product. Results will show good agreement with the Advanced Driver Assistance Systems data in the ability to estimate road grade between any two consecutive points in the contiguous United States.	+	urate and reliable global positioning system (GPS)-based vehicle use data are highly valuable for many transportation, analysis, and automotive considerations. Model-based design, real-world fuel economy analysis, and the growing field of autonomous and connected technologies (including predictive powertrain control and self-driving cars) all have a vested interest in high-fidelity estimation of powertrain loads and vehicle usage profiles. Unfortunately, road grade can be a difficult property to extract from GPS data with consistency. In this report, we present a methodology for appending high-resolution elevation data to GPS speed traces via a static digital elevation model. Anomalous data points in the digital elevation model are addressed during a filtration/smoothing routine, resulting in an elevation profile that can be used to calculate road grade. This process is evaluated against a large, commercially available height/slope dataset from the Navteq/Nokia/HERE Advanced Driver Assistance Systems product. Results will show good agreement with the Advanced Driver Assistance Systems data in the ability to estimate road grade between any two consecutive points in the contiguous United States.

@@ Line 3: / Line 3: @@
 Accurate and reliable global positioning system (GPS)-based vehicle use data are highly valuable for many transportation, analysis, and automotive considerations. Model-based design, real-world fuel economy analysis, and the growing field of autonomous and connected technologies (including predictive powertrain control and self-driving cars) all have a vested interest in high-fidelity estimation of powertrain loads and vehicle usage profiles. Unfortunately, road grade can be a difficult property to extract from GPS data with consistency. In this report, we present a methodology for appending high-resolution elevation data to GPS speed traces via a static digital elevation model. Anomalous data points in the digital elevation model are addressed during a filtration/smoothing routine, resulting in an elevation profile that can be used to calculate road grade. This process is evaluated against a large, commercially available height/slope dataset from the Navteq/Nokia/HERE Advanced Driver Assistance Systems product. Results will show good agreement with the Advanced Driver Assistance Systems data in the ability to estimate road grade between any two consecutive points in the contiguous United States.
@@ Line 16: / Line 11: @@
 * [http://dx.doi.org/10.2172/1134504 http://dx.doi.org/10.2172/1134504]
-* [http://pdfs.semanticscholar.org/d893/d1b45dac01ed664a6651930e3c8fefc496f3.pdf http://pdfs.semanticscholar.org/d893/d1b45dac01ed664a6651930e3c8fefc496f3.pdf]
+* [https://www.nrel.gov/docs/fy14osti/61109.pdf https://www.nrel.gov/docs/fy14osti/61109.pdf]
-* [http://www.nrel.gov/docs/fy14osti/61109.pdf http://www.nrel.gov/docs/fy14osti/61109.pdf],[https://academic.microsoft.com/#/detail/2290841126 https://academic.microsoft.com/#/detail/2290841126]
+* [https://www.nrel.gov/docs/fy14osti/61109.pdf https://www.nrel.gov/docs/fy14osti/61109.pdf],

← Older revision	Revision as of 09:18, 26 October 2020
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