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Minimizing Long Vehicles Overhang Exceeding the Drivable Surface via Convex Path Optimization
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.ORCID iD: 0000-0002-6802-7520
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.ORCID iD: 0000-0002-3672-5316
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.ORCID iD: 0000-0002-1927-1690
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel path planning algo- rithm for on-road autonomous driving. The algorithm targets long and wide vehicles, in which the overhangs (i.e., the vehicle chassis extending beyond the front and rear wheelbase) can endanger other vehicles, pedestrians, or even the vehicle itself. The vehicle motion is described in a road-aligned coordinate frame. A novel method for computing the vehicle limits is proposed guaranteeing feasibility of the planned path when con- verted back into the original coordinate frame. The algorithm is posed as a convex optimization that takes into account the exact dimensions of the vehicle and the road, while minimizing the amount of overhang outside of the drivable surface.

The results of the proposed algorithm are compared in a simulation of a real road scenario against a centerline tracking scheme. The results show a significant decrease on the amount of overhang area outside of the drivable surface, leading to an increased safety in driving maneuvers. The real-time applicability of the method is shown, by using it in a receding- horizon framework. 

Place, publisher, year, edition, pages
IEEE, 2017.
Keyword [en]
Path planning, Optimization, Autonomous Driving
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-220575OAI: oai:DiVA.org:kth-220575DiVA, id: diva2:1169479
Conference
IEEE Intelligent Transportation Systems Conference
Note

QC 20180119

Available from: 2017-12-27 Created: 2017-12-27 Last updated: 2018-03-27Bibliographically approved

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Lima, Pedro F.Mårtensson, JonasWahlberg, Bo
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