Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking with Visibility Maximization
© 2016 IEEE. In this article we present a trajectory generation method for autonomous overtaking of unexpected obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example when overtaking unexpected objects on the vehicle's ego lane...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135931 |
| _version_ | 1826192518627196928 |
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| author | Andersen, Hans Alonso-Mora, Javier Eng, You Hong Rus, Daniela Ang, Marcelo H |
| author2 | Singapore-MIT Alliance in Research and Technology (SMART) |
| author_facet | Singapore-MIT Alliance in Research and Technology (SMART) Andersen, Hans Alonso-Mora, Javier Eng, You Hong Rus, Daniela Ang, Marcelo H |
| author_sort | Andersen, Hans |
| collection | MIT |
| description | © 2016 IEEE. In this article we present a trajectory generation method for autonomous overtaking of unexpected obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example when overtaking unexpected objects on the vehicle's ego lane on a two-way street. In this case, a human driver would first make sure that the opposite lane is free and that there is enough room to successfully execute the maneuver, and then it would cut into the opposite lane in order to execute the maneuver successfully. We consider the practical problem of autonomous overtaking when the coverage of the perception system is impaired due to occlusion. Safe trajectories are generated by solving, in real-time, a non-linear constrained optimization, formulated as a receding horizon planner that maximizes the ego vehicle's visibility. The planner is complemented by a high-level behavior planner, which takes into account the occupancy of other traffic participants, the information from the vehicle's perception system, and the risk associated with the overtaking maneuver, to determine when the overtake maneuver should happen. The approach is validated in simulation and in experiments in real world traffic. |
| first_indexed | 2024-09-23T09:18:31Z |
| format | Article |
| id | mit-1721.1/135931 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:18:31Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1359312023-03-01T21:04:25Z Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking with Visibility Maximization Andersen, Hans Alonso-Mora, Javier Eng, You Hong Rus, Daniela Ang, Marcelo H Singapore-MIT Alliance in Research and Technology (SMART) Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 2016 IEEE. In this article we present a trajectory generation method for autonomous overtaking of unexpected obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example when overtaking unexpected objects on the vehicle's ego lane on a two-way street. In this case, a human driver would first make sure that the opposite lane is free and that there is enough room to successfully execute the maneuver, and then it would cut into the opposite lane in order to execute the maneuver successfully. We consider the practical problem of autonomous overtaking when the coverage of the perception system is impaired due to occlusion. Safe trajectories are generated by solving, in real-time, a non-linear constrained optimization, formulated as a receding horizon planner that maximizes the ego vehicle's visibility. The planner is complemented by a high-level behavior planner, which takes into account the occupancy of other traffic participants, the information from the vehicle's perception system, and the risk associated with the overtaking maneuver, to determine when the overtake maneuver should happen. The approach is validated in simulation and in experiments in real world traffic. 2021-10-27T20:30:00Z 2021-10-27T20:30:00Z 2020 2021-04-02T13:35:01Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135931 en 10.1109/TIV.2019.2955361 IEEE Transactions on Intelligent Vehicles Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other repository |
| spellingShingle | Andersen, Hans Alonso-Mora, Javier Eng, You Hong Rus, Daniela Ang, Marcelo H Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking with Visibility Maximization |
| title | Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking with Visibility Maximization |
| title_full | Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking with Visibility Maximization |
| title_fullStr | Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking with Visibility Maximization |
| title_full_unstemmed | Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking with Visibility Maximization |
| title_short | Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking with Visibility Maximization |
| title_sort | trajectory optimization and situational analysis framework for autonomous overtaking with visibility maximization |
| url | https://hdl.handle.net/1721.1/135931 |
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