dc.contributor.author |
Berman, R
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|
dc.contributor.author |
Benade, R
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|
dc.contributor.author |
Rosman, Benjamin S
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|
dc.date.accessioned |
2016-07-22T07:42:46Z |
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dc.date.available |
2016-07-22T07:42:46Z |
|
dc.date.issued |
2015-11 |
|
dc.identifier.citation |
Berman, R. Benade, R. and Rosman, B.S. 2015. Autonomous prediction of performance-based standards for heavy vehicles. In: PRASA-RobMech International Conference, Port Elizabeth, 26-27 November 2015 |
en_US |
dc.identifier.uri |
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7359520&tag=1
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/8679
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|
dc.description |
PRASA-RobMech International Conference, Port Elizabeth, 26-27 November 2015. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website |
en_US |
dc.description.abstract |
In most countries throughout the world, heavy vehicle use on public roads are governed by prescriptive rules, typically by imposing stringent mass and dimension limits in an attempt to control vehicle safety. A recent alternative framework is a performance-based standards approach which specifies on-road vehicle performance measures. One such standard is the low-speed swept path, which is a measure of road width required by a vehicle to complete a prescribed turning manoeuvre. This is typically determined by physical testing or detailed vehicle simulations, both of which are costly and time consuming processes. This paper presents a data driven, detailed model to predict the low-speed performance of an articulated vehicle, given only the vehicle geometry. The development of a lightweight tool to predict the swept path of an articulated heavy vehicle, without the need for detailed simulation or testing, is discussed. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE Xplore |
en_US |
dc.relation.ispartofseries |
Workflow;15973 |
|
dc.subject |
Performance-based standards |
en_US |
dc.subject |
Vehicle safety |
en_US |
dc.subject |
Heavy vehicle performance |
en_US |
dc.subject |
Regression |
en_US |
dc.subject |
Support vector machines |
en_US |
dc.title |
Autonomous prediction of performance-based standards for heavy vehicles |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Berman, R., Benade, R., & Rosman, B. S. (2015). Autonomous prediction of performance-based standards for heavy vehicles. IEEE Xplore. http://hdl.handle.net/10204/8679 |
en_ZA |
dc.identifier.chicagocitation |
Berman, R, R Benade, and Benjamin S Rosman. "Autonomous prediction of performance-based standards for heavy vehicles." (2015): http://hdl.handle.net/10204/8679 |
en_ZA |
dc.identifier.vancouvercitation |
Berman R, Benade R, Rosman BS, Autonomous prediction of performance-based standards for heavy vehicles; IEEE Xplore; 2015. http://hdl.handle.net/10204/8679 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Berman, R
AU - Benade, R
AU - Rosman, Benjamin S
AB - In most countries throughout the world, heavy vehicle use on public roads are governed by prescriptive rules, typically by imposing stringent mass and dimension limits in an attempt to control vehicle safety. A recent alternative framework is a performance-based standards approach which specifies on-road vehicle performance measures. One such standard is the low-speed swept path, which is a measure of road width required by a vehicle to complete a prescribed turning manoeuvre. This is typically determined by physical testing or detailed vehicle simulations, both of which are costly and time consuming processes. This paper presents a data driven, detailed model to predict the low-speed performance of an articulated vehicle, given only the vehicle geometry. The development of a lightweight tool to predict the swept path of an articulated heavy vehicle, without the need for detailed simulation or testing, is discussed.
DA - 2015-11
DB - ResearchSpace
DP - CSIR
KW - Performance-based standards
KW - Vehicle safety
KW - Heavy vehicle performance
KW - Regression
KW - Support vector machines
LK - https://researchspace.csir.co.za
PY - 2015
T1 - Autonomous prediction of performance-based standards for heavy vehicles
TI - Autonomous prediction of performance-based standards for heavy vehicles
UR - http://hdl.handle.net/10204/8679
ER -
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en_ZA |