Testing Drivers Skill for High-Speed Autonomous Vehicles.
The article provides information on the innovative tests developed by the Red Team of Carnegie Mellon University that were effective tools for measuring the driving skill of autonomous ground vehicles (AGV) in the 2005 Defense Advanced Research Projects Agency Grand Challenge in the U.S. The blind p...
| Publicado en: | Computer (00189162) Vol. 39; no. 12; pp. 48 - 52 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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IEEE
Dec2006
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=23555741&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 23555741 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: Dec2006 vid: 39 iid: 12 pid: 13605 pub: IEEE artinfo: ui: 23555741 10.1109/MC.2006.444 ppf: 48 ppct: 4 formats: tig: atl: Testing Drivers Skill for High-Speed Autonomous Vehicles. aug: au: Urmson, Chris Whittaker, William "Red" Harbaugh, Sam Clark, Michael Koon, Phillip affil: Carnegie Mellon University's Director of Technology Fredkin Professor of Robotics Director of the Field Robotics Center Systems Engineer for Tartan Racing, Carnegie Mellon's Urban Challenge Team Teaches Systems Engineering, Carnegie Mellon Senior Systems Engineer, Harris Corp Member, Mechatronics Center of Excellence Senior Principal Engineer for Boeing Co. Chief Engineer for Team Urbanator Boeing's DARPA Urban Challenge su: Computer software testing Remotely piloted vehicles Automobile driving Software measurement Software architecture Computer input-output equipment Carnegie-Mellon University United States. Defense Advanced Research Projects Agency United States sug: subj: United States Computer software testing Remotely piloted vehicles Automobile driving Software measurement Software architecture Computer input-output equipment Carnegie-Mellon University United States. Defense Advanced Research Projects Agency ab: The article provides information on the innovative tests developed by the Red Team of Carnegie Mellon University that were effective tools for measuring the driving skill of autonomous ground vehicles (AGV) in the 2005 Defense Advanced Research Projects Agency Grand Challenge in the U.S. The blind path-tracking, perception-tracking, and perception-planning tests evaluate how the unit changes in hardware and software affected the driving ability of the robots. The blind-tracking test is useful in measuring the ability of an AGV to blindly follow waypoints, while perception-tracking is a good tool for measuring the effects of perception on path-tracking and the perception-planning test is useful in measuring the capability of AGV to dynamically adapt to items impeding the preplanned path. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2006 holdings: @attributes: islocal: N |
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