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- Publisher Website: 10.1109/ICRA.2014.6907209
- Scopus: eid_2-s2.0-84929191688
- WOS: WOS:000377221102087
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Conference Paper: Motion planning under uncertainty for on-road autonomous driving
Title | Motion planning under uncertainty for on-road autonomous driving |
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Authors | |
Issue Date | 2014 |
Citation | 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 31 May-7 June 2014. In Conference Proceedings, 2014, p. 2507-2512 How to Cite? |
Abstract | We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants. The future motion of traffic participants is predicted using a local planner, and the uncertainty along the predicted trajectory is computed based on Gaussian propagation. For the autonomous vehicle, the uncertainty from localization and control is estimated based on a Linear-Quadratic Gaussian (LQG) framework. Compared with other safety assessment methods, our framework allows the planner to avoid unsafe situations more efficiently, thanks to the direct uncertainty information feedback to the planner. We also demonstrate our planner's ability to generate safer trajectories compared to planning only with a LQG framework. |
Persistent Identifier | http://hdl.handle.net/10722/308852 |
ISSN | 2023 SCImago Journal Rankings: 1.620 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xu, Wenda | - |
dc.contributor.author | Pan, Jia | - |
dc.contributor.author | Wei, Junqing | - |
dc.contributor.author | Dolan, John M. | - |
dc.date.accessioned | 2021-12-08T07:50:16Z | - |
dc.date.available | 2021-12-08T07:50:16Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 31 May-7 June 2014. In Conference Proceedings, 2014, p. 2507-2512 | - |
dc.identifier.issn | 1050-4729 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308852 | - |
dc.description.abstract | We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants. The future motion of traffic participants is predicted using a local planner, and the uncertainty along the predicted trajectory is computed based on Gaussian propagation. For the autonomous vehicle, the uncertainty from localization and control is estimated based on a Linear-Quadratic Gaussian (LQG) framework. Compared with other safety assessment methods, our framework allows the planner to avoid unsafe situations more efficiently, thanks to the direct uncertainty information feedback to the planner. We also demonstrate our planner's ability to generate safer trajectories compared to planning only with a LQG framework. | - |
dc.language | eng | - |
dc.relation.ispartof | 2014 IEEE International Conference on Robotics and Automation (ICRA) | - |
dc.title | Motion planning under uncertainty for on-road autonomous driving | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICRA.2014.6907209 | - |
dc.identifier.scopus | eid_2-s2.0-84929191688 | - |
dc.identifier.spage | 2507 | - |
dc.identifier.epage | 2512 | - |
dc.identifier.isi | WOS:000377221102087 | - |