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Autonomous prediction of performance-based standards for heavy vehicles

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dc.contributor.author Berman, R
dc.contributor.author Benade, R
dc.contributor.author Rosman, Benjamin S
dc.date.accessioned 2016-07-22T07:42:46Z
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
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 - en_ZA


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