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Combining Lattice-Based Planning and Path Optimization in Autonomous Heavy Duty Vehicle Applications
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Scania, Autonomous Transport Solutions, Sodertalje, Sweden.
Scania.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-3672-5316
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-1927-1690
2018 (English)In: 2018 IEEE Intelligent Vehicles Symposium (IV), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 2090-2097, article id 8500616Conference paper, Published paper (Refereed)
Abstract [en]

Lattice-based motion planners are an established method to generate feasible motions for car-like vehicles. However, the solution paths can only reach a discretized approximation of the intended goal pose. Moreover, they can be optimal only with respect to the actions available to the planner, which can result in paths with excessive steering. These drawbacks have a negative impact when used in real systems. In this paper we address both drawbacks by integrating a steering method into a state-of-the-art lattice-based motion planner. Un- like previous approaches, in which path optimization happens in an a posteriori step after the planner has found a solution, we propose an interleaved execution of path planning and path optimization. The proposed approach can run in real-time and is implemented in a full-size autonomous truck, and we show experimentally that it is able to greatly improve the quality of the solutions provided by a lattice planner.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 2090-2097, article id 8500616
Keywords [en]
Intelligent vehicle highway systems, Motion planning, Steering, Car-like vehicles, Heavy duty vehicles, Lattice planners, Lattice-based, Motion planners, Path optimizations, State of the art, Steering method, Automobile steering equipment
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-247124DOI: 10.1109/IVS.2018.8500616Scopus ID: 2-s2.0-85056776487ISBN: 9781538644522 (print)OAI: oai:DiVA.org:kth-247124DiVA, id: diva2:1301883
Conference
2018 IEEE Intelligent Vehicles Symposium, IV 2018; Changshu, Suzhou; China; 26 September 2018 through 30 September 2018,26-30 June 2018, Changshu, China
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2020-02-14Bibliographically approved

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Oliveira, Rui Filipe De SousaMårtensson, JonasWahlberg, Bo

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