A Review of Motion Planning Techniques for Automated Vehicles
Abstract
Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.
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State lattices can handle high dimensions and are good for local planning in dynamical environments, but their computational load is high and the discretization resolution limits the planners' capacity.
“State lattices can handle high dimensions and is good for local planning in dynamical environments, however, the computational load is high and the discretization resolution limits the planners' capacity [19].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [19]
The main disadvantage of sampling-based planning in general is jerky trajectories.
“The main disadvantage of SBP in general is, again, the jerky trajectories [19].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [19]
Graph-based local planners output discrete paths rather than continuous ones, which can lead to jerky trajectories.
“Graph-based local planners use the same techniques as graph-based global planners such as Dijkstra [254] and A* [249], which output discrete paths rather than continuous ones.This can lead to jerky trajectories [19].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [19]
A detailed local planning review divided the taxonomy of motion planning into four groups: graph-based planners, sampling-based planners, interpolating curve planners and numerical optimization approaches.
“A detailed local planning review is presented in [19] where the taxonomy of motion planning was divided into four groups; graph-based planners, sampling-based planners, interpolating curve planners and numerical optimization approaches.”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [19]
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Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889 · see citing works
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- Authors
- Gonzalez, D., Perez, J., Milanes, V., & Nashashibi, F.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- ORCID
- Nashashibi, Fawzi
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2016-04
- Volume
- 17
- Issue
- 4
- Pages
- 1135-1145
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2306644740
- Cited by
- 1,670
- References
- 3
- Citations by year
- 2026: 104 · 2025: 163 · 2024: 200 · 2023: 246 · 2022: 212 · 2021: 235 · 2020: 213 · 2019: 150 · 2018: 93 · 2017: 47 · 2016: 6 · 2012: 1
- FWCI
- 41.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- HAL (submittedVersion)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion)
- Funders
- ECSEL project DESERVE.
- FP7 CityMobil2.
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Keywords
- motion planning, autonomous vehicles, autonomous driving, obstacle avoidance, overtaking, vulnerable road users, semi-automated vehicles, energy optimization, vehicle safety
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