Real-Time Motion Planning Approach for Automated Driving in Urban Environments
Autonomous vehicles must be able to react in a timely manner to typical and unpredictable situations in urban scenarios. In this connection, motion planning algorithms play a key role as they are responsible of ensuring driving safety and comfort while producing human-like trajectories in a wide ran...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8932495/ |
| _version_ | 1818877611014619136 |
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| author | Antonio Artunedo Jorge Villagra Jorge Godoy |
| author_facet | Antonio Artunedo Jorge Villagra Jorge Godoy |
| author_sort | Antonio Artunedo |
| collection | DOAJ |
| description | Autonomous vehicles must be able to react in a timely manner to typical and unpredictable situations in urban scenarios. In this connection, motion planning algorithms play a key role as they are responsible of ensuring driving safety and comfort while producing human-like trajectories in a wide range of driving scenarios. Typical approaches for motion planning focus on trajectory optimization by applying computation-intensive algorithms, rather than finding a balance between optimatily and computing time. However, for on-road automated driving at medium and high speeds, determinism is necessary at high sampling rates. This work presents a trajectory planning algorithm that is able to provide safe, human-like and comfortable trajectories by using cost-effective primitives evaluation based on quintic Bézier curves. The proposed method is able to consider the kinodynamic constrains of the vehicle while reactively handling dynamic real environments in real-time. The proposed motion planning strategy has been implemented in a real experimental platform and validated in different real operating environments, successfully overcoming typical urban traffic scenes where both static and dynamic objects are involved. |
| first_indexed | 2024-12-19T14:01:02Z |
| format | Article |
| id | doaj.art-5c2b480e89ab48c2a6afea8530917932 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T14:01:02Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5c2b480e89ab48c2a6afea85309179322022-12-21T20:18:27ZengIEEEIEEE Access2169-35362019-01-01718003918005310.1109/ACCESS.2019.29594328932495Real-Time Motion Planning Approach for Automated Driving in Urban EnvironmentsAntonio Artunedo0https://orcid.org/0000-0003-2161-9876Jorge Villagra1https://orcid.org/0000-0002-3963-7952Jorge Godoy2https://orcid.org/0000-0002-3132-5348Centre for Automation and Robotics (CSIC-UPM), Arganda del Rey, SpainCentre for Automation and Robotics (CSIC-UPM), Arganda del Rey, SpainCentre for Automation and Robotics (CSIC-UPM), Arganda del Rey, SpainAutonomous vehicles must be able to react in a timely manner to typical and unpredictable situations in urban scenarios. In this connection, motion planning algorithms play a key role as they are responsible of ensuring driving safety and comfort while producing human-like trajectories in a wide range of driving scenarios. Typical approaches for motion planning focus on trajectory optimization by applying computation-intensive algorithms, rather than finding a balance between optimatily and computing time. However, for on-road automated driving at medium and high speeds, determinism is necessary at high sampling rates. This work presents a trajectory planning algorithm that is able to provide safe, human-like and comfortable trajectories by using cost-effective primitives evaluation based on quintic Bézier curves. The proposed method is able to consider the kinodynamic constrains of the vehicle while reactively handling dynamic real environments in real-time. The proposed motion planning strategy has been implemented in a real experimental platform and validated in different real operating environments, successfully overcoming typical urban traffic scenes where both static and dynamic objects are involved.https://ieeexplore.ieee.org/document/8932495/Autonomous drivingtrajectory planningobstacle avoidancecollision checking |
| spellingShingle | Antonio Artunedo Jorge Villagra Jorge Godoy Real-Time Motion Planning Approach for Automated Driving in Urban Environments IEEE Access Autonomous driving trajectory planning obstacle avoidance collision checking |
| title | Real-Time Motion Planning Approach for Automated Driving in Urban Environments |
| title_full | Real-Time Motion Planning Approach for Automated Driving in Urban Environments |
| title_fullStr | Real-Time Motion Planning Approach for Automated Driving in Urban Environments |
| title_full_unstemmed | Real-Time Motion Planning Approach for Automated Driving in Urban Environments |
| title_short | Real-Time Motion Planning Approach for Automated Driving in Urban Environments |
| title_sort | real time motion planning approach for automated driving in urban environments |
| topic | Autonomous driving trajectory planning obstacle avoidance collision checking |
| url | https://ieeexplore.ieee.org/document/8932495/ |
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