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dc.contributor.author Lee, Hyeongyu -
dc.contributor.author Park, Jaegeun -
dc.contributor.author Koo, Changjin -
dc.contributor.author Kim, Jong-Chan -
dc.contributor.author Eun, Yongsoon -
dc.date.accessioned 2023-12-26T18:13:35Z -
dc.date.available 2023-12-26T18:13:35Z -
dc.date.created 2022-09-08 -
dc.date.issued 2022-05-23 -
dc.identifier.isbn 9781728196817 -
dc.identifier.issn 1050-4729 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/46844 -
dc.description.abstract Cyclops, introduced in this paper, is an open research platform for everyone who wants to validate novel ideas and approaches in self-driving heavy-duty vehicle platooning. The platform consists of multiple 1/14 scale semi-trailer trucks equipped with associated computing, communication and control modules that enable self-driving on our scale proving ground. The perception system for each vehicle is composed of a lidar-based object tracking system and a lane detection/control system. The former maintains the gap to the leading vehicle, and the latter maintains the vehicle within the lane by steering control. The lane detection system is optimized for truck platooning, where the field of view of the front-facing camera is severely limited due to a small gap to the leading vehicle. This platform is particularly amenable to validating mitigation strategies for safety-critical situations. Indeed, the simplex architecture is adopted in the computing modules, enabling various fail-safe operations. In particular, we illustrate a scenario where the camera sensor fails in the perception system, but the vehicle is able to operate at a reduced capacity to a graceful stop. Details of Cyclops, including 3D CAD designs and algorithm source codes, are released for those who want to build similar testbeds. © 2022 IEEE. -
dc.language English -
dc.publisher IEEE Robotics and Automation Society (RA) -
dc.relation.ispartof Proceedings - IEEE International Conference on Robotics and Automation -
dc.title Cyclops: Open Platform for Scale Truck Platooning -
dc.type Conference Paper -
dc.identifier.doi 10.1109/ICRA46639.2022.9812174 -
dc.identifier.wosid 000941277601121 -
dc.identifier.scopusid 2-s2.0-85136323702 -
dc.identifier.bibliographicCitation IEEE International Conference on Robotics and Automation, pp.8971 - 8977 -
dc.identifier.url https://www.icra2022.org/ -
dc.citation.conferenceDate 2022-05-23 -
dc.citation.conferencePlace US -
dc.citation.conferencePlace Philadelphia, PA -
dc.citation.endPage 8977 -
dc.citation.startPage 8971 -
dc.citation.title IEEE International Conference on Robotics and Automation -
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Department of Electrical Engineering and Computer Science DSC Lab(Dynamic Systems and Control Laboratory) 2. Conference Papers

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