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Article

Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios

Autopia Program, Centre for Automation and Robotics, CSIC-UPM, Ctra. M300 Campo Real, Km 0.200, Arganda del Rey, 28500 Madrid, Spain
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Author to whom correspondence should be addressed.
Academic Editor: Joon-Sang Park
Sensors 2021, 21(11), 3755; https://doi.org/10.3390/s21113755
Received: 14 April 2021 / Revised: 18 May 2021 / Accepted: 24 May 2021 / Published: 28 May 2021
(This article belongs to the Special Issue Smooth Motion Planning for Autonomous Vehicles)
Safe and adaptable motion planning for autonomous vehicles remains an open problem in urban environments, where the variability of situations and behaviors may become intractable using rule-based approaches. This work proposes a use-case-independent motion planning algorithm that generates a set of possible trajectories and selects the best of them according to a merit function that combines longitudinal comfort, lateral comfort, safety and utility criteria. The system was tested in urban scenarios on simulated and real environments, and the results show that different driving styles can be achieved according to the priorities set in the merit function, always meeting safety and comfort parameters imposed by design. View Full-Text
Keywords: autonomous driving; motion planning; trajectory generation; speed profile; merit function autonomous driving; motion planning; trajectory generation; speed profile; merit function
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MDPI and ACS Style

Medina-Lee, J.; Artuñedo, A.; Godoy, J.; Villagra, J. Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios. Sensors 2021, 21, 3755. https://doi.org/10.3390/s21113755

AMA Style

Medina-Lee J, Artuñedo A, Godoy J, Villagra J. Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios. Sensors. 2021; 21(11):3755. https://doi.org/10.3390/s21113755

Chicago/Turabian Style

Medina-Lee, Juan, Antonio Artuñedo, Jorge Godoy, and Jorge Villagra. 2021. "Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios" Sensors 21, no. 11: 3755. https://doi.org/10.3390/s21113755

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