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A unified motion planning method for parking an autonomous vehicle in the presence of irregularly placed obstacles
Open accessAbstract
This paper proposes a motion planner for autonomous parking. Compared to the prevailing and emerging studies that handle specific or regular parking scenarios only, our method describes various kinds of parking cases in a unified way regardless they are regular parking scenarios (e.g., parallel, perpendicular or echelon parking cases) or not. First, we formulate a time-optimal dynamic optimization problem with vehicle kinematics, collision-avoidance conditions and mechanical constraints strictly described. Thereafter, an interior-point simultaneous approach is introduced to solve that formulated dynamic optimization problem. Simulation results validate that our proposed motion planning method can tackle general parking scenarios. The tested parking scenarios in this paper can be regarded as benchmark cases to evaluate the efficiency of methods that may emerge in the future. Our established dynamic optimization problem is an open and unified framework, where other complicated user-specific constraints/optimization criteria can be handled without additional difficulty, provided that they are expressed through inequalities/polynomial explicitly. This proposed motion planner may be suitable for the next-generation intelligent parking-garage system.
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“Relevant products have already been made available in the market by many of the original equipment manufacturers such as T esla, Volvo, Audi, BMW, Ford, Land Rover, Mercedes-Benz, Nissan, and T oyota”
From Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy (2019) · cites this paper as Li & Shao, 2015
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Metadata
- Authors
- Li, B., & Shao, Z.
- Affiliations
- Zhejiang University (CN)
- State Key Laboratory of Industrial Control Technology (CN)
- Corresponding author
- Shao, Zhijiang
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2015-09
- Volume
- 86
- Pages
- 11-20
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2027311349
- Cited by
- 195
- References
- 35
- Citations by year
- 2026: 13 · 2025: 31 · 2024: 17 · 2023: 26 · 2022: 17 · 2021: 20 · 2020: 15 · 2019: 18 · 2018: 11 · 2017: 13 · 2016: 8 · 2015: 6
- FWCI
- 10.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Knowledge-Based Systems (publishedVersion, cc-by)
- Funders
- Program of China (2012CB720503)
- National Nature Science Foundation (61374167)
- 6th National College Students’ Innovation & Entrepreneurial Training Program (201210006050)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- autonomous parking, motion planning, irregular obstacles, time-optimal control, dynamic optimization, vehicle kinematics, collision avoidance, interior point methods, parallel parking, perpendicular parking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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