Anytime Optimal Trajectory Repairing for Autonomous Vehicles
Adapting to dynamically changing situations remains a pivotal challenge for automated driving systems, which demand robust and efficient solutions. Occasional perception errors inherent in artificial intelligence further complicate the task. Whereas traditional motion planning algorithms address thi...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10979545/ |
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| _version_ | 1849723153166630912 |
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| author | Kailin Tong Martin Steinberger Martin Horn Selim Solmaz Daniel Watzenig |
| author_facet | Kailin Tong Martin Steinberger Martin Horn Selim Solmaz Daniel Watzenig |
| author_sort | Kailin Tong |
| collection | DOAJ |
| description | Adapting to dynamically changing situations remains a pivotal challenge for automated driving systems, which demand robust and efficient solutions. Occasional perception errors inherent in artificial intelligence further complicate the task. Whereas traditional motion planning algorithms address this challenge by replanning the entire trajectory, a significantly more efficient strategy is to repair only the flawed segments. Our paper introduces a groundbreaking approach by formulating an optimal trajectory repairing problem and proposing an innovative and efficient framework for critical timing detection and trajectory repairing. This trajectory repairing specifically employs Bernstein basis polynomials in both 2D distance-time and 3D spatiotemporal spaces. A distinctive feature of our method is the use of an anytime grid search to determine a sub-optimal time-to-repair, which contrasts with previous methods that relied on manually tuned or fixed repair times, limiting both flexibility and robustness. A statistical analysis of 100 scenarios demonstrates that our trajectory-repairing framework outperforms the path-speed decoupled repairing framework in terms of scenario success rate. Furthermore, we introduce a novel algorithm for driving corridor generation that more accurately approximates the collision-free space than state-of-the-art work. The proposed approach has broad potential for application in embedded systems across various autonomous platforms. |
| format | Article |
| id | doaj-art-ab66ae327a3d41a3a1541c6d1a9c968b |
| institution | DOAJ |
| issn | 2687-7813 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-ab66ae327a3d41a3a1541c6d1a9c968b2025-08-20T03:11:06ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132025-01-01653755310.1109/OJITS.2025.356382310979545Anytime Optimal Trajectory Repairing for Autonomous VehiclesKailin Tong0https://orcid.org/0000-0002-7040-9237Martin Steinberger1https://orcid.org/0000-0001-6545-3949Martin Horn2https://orcid.org/0000-0002-5845-1061Selim Solmaz3https://orcid.org/0000-0003-0686-1306Daniel Watzenig4https://orcid.org/0000-0002-5341-9708Department of Electrics, Electronics, and Software, Virtual Vehicle Research GmbH, Graz, AustriaInstitute of Automation and Control, Graz University of Technology, Graz, AustriaInstitute of Automation and Control, Graz University of Technology, Graz, AustriaDepartment of Electrics, Electronics, and Software, Virtual Vehicle Research GmbH, Graz, AustriaDepartment of Electrics, Electronics, and Software, Virtual Vehicle Research GmbH, Graz, AustriaAdapting to dynamically changing situations remains a pivotal challenge for automated driving systems, which demand robust and efficient solutions. Occasional perception errors inherent in artificial intelligence further complicate the task. Whereas traditional motion planning algorithms address this challenge by replanning the entire trajectory, a significantly more efficient strategy is to repair only the flawed segments. Our paper introduces a groundbreaking approach by formulating an optimal trajectory repairing problem and proposing an innovative and efficient framework for critical timing detection and trajectory repairing. This trajectory repairing specifically employs Bernstein basis polynomials in both 2D distance-time and 3D spatiotemporal spaces. A distinctive feature of our method is the use of an anytime grid search to determine a sub-optimal time-to-repair, which contrasts with previous methods that relied on manually tuned or fixed repair times, limiting both flexibility and robustness. A statistical analysis of 100 scenarios demonstrates that our trajectory-repairing framework outperforms the path-speed decoupled repairing framework in terms of scenario success rate. Furthermore, we introduce a novel algorithm for driving corridor generation that more accurately approximates the collision-free space than state-of-the-art work. The proposed approach has broad potential for application in embedded systems across various autonomous platforms.https://ieeexplore.ieee.org/document/10979545/Autonomous vehiclescollision avoidancetrajectory planningvehicle safety |
| spellingShingle | Kailin Tong Martin Steinberger Martin Horn Selim Solmaz Daniel Watzenig Anytime Optimal Trajectory Repairing for Autonomous Vehicles IEEE Open Journal of Intelligent Transportation Systems Autonomous vehicles collision avoidance trajectory planning vehicle safety |
| title | Anytime Optimal Trajectory Repairing for Autonomous Vehicles |
| title_full | Anytime Optimal Trajectory Repairing for Autonomous Vehicles |
| title_fullStr | Anytime Optimal Trajectory Repairing for Autonomous Vehicles |
| title_full_unstemmed | Anytime Optimal Trajectory Repairing for Autonomous Vehicles |
| title_short | Anytime Optimal Trajectory Repairing for Autonomous Vehicles |
| title_sort | anytime optimal trajectory repairing for autonomous vehicles |
| topic | Autonomous vehicles collision avoidance trajectory planning vehicle safety |
| url | https://ieeexplore.ieee.org/document/10979545/ |
| work_keys_str_mv | AT kailintong anytimeoptimaltrajectoryrepairingforautonomousvehicles AT martinsteinberger anytimeoptimaltrajectoryrepairingforautonomousvehicles AT martinhorn anytimeoptimaltrajectoryrepairingforautonomousvehicles AT selimsolmaz anytimeoptimaltrajectoryrepairingforautonomousvehicles AT danielwatzenig anytimeoptimaltrajectoryrepairingforautonomousvehicles |