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Motion Planning for Heavy-Duty Vehicles
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
2019 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

Autonomous driving is a disrupting technology that is expected to reshape transportation systems. The benefits of autonomous vehicles include, but are not limited to, safer transportation, increased economic growth, and broader access to mobility services. Industry and academia are currently researching a variety of topics related to autonomous driving, however, the focus seems to be on passenger vehicles. As a consequence, heavy-duty vehicles, which are a significant share of transportation systems, are overlooked, and the challenges associated with these vehicles are neglected.

This thesis studies motion planning algorithms for heavy-duty vehicles. Motion planning is a fundamental part of autonomous vehicles, it is tasked with finding the correct sequence of actions that take the vehicle towards its goal. This work focuses on particular aspects that distinguish heavy-duty vehicles from passenger vehicles, and that call for novel developments within motion planning algorithms.

We start by addressing the problem of finding shortest paths for a vehicle in obstacle-free environments. This problem has been studied since the fifties, but the addressed vehicle models are often simplistic. We propose a novel algorithm that is able to plan paths respecting complex vehicle actuator constraints associated with the slow dynamics of heavy vehicles.

Using the previous method, we tackle the motion planning problem in environments populated with obstacles. Lattice-based motion planners, a popular choice for this type of scenario, come with drawbacks related to the sub-optimality of solution paths, and the discretization of the goal state. We propose a novel path optimization method, which is able to significantly reduce both problems. The resulting optimized paths contain less oscillatory behavior and arrive precisely at arbitrary non-discretized goal states.

We then study the problem of bus driving in urban environments. It is shown how this type of driving is fundamentally different than that of other vehicles, due to the chassis configuration with large overhangs. To successfully maneuver buses, distinct driving objectives need to be used in planning algorithms. Moreover, a novel environment classification scheme must be introduced. The result is a motion planning algorithm that is able to mimic professional bus driver behavior, resulting in safer driving and increased vehicle maneuverability.

 

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2019. , p. 132
Series
TRITA-EECS-AVL ; 2019:56
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-252537ISBN: 978-91-7873-244-9 (print)OAI: oai:DiVA.org:kth-252537DiVA, id: diva2:1319346
Presentation
2019-08-20, L1, Drottning Kristinas väg 30, KTH, Stockholm, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

QC 20190603

Available from: 2019-06-03 Created: 2019-05-31 Last updated: 2019-06-03Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
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More styles
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
  • rtf