Transferable Pedestrian Motion Prediction Models at Intersections
© 2018 IEEE. One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one inte...
| 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/1721.1/137874.2 |
| _version_ | 1811081330805440512 |
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| author | Shen, Macheng Habibi, Golnaz How, Jonathan P. |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Shen, Macheng Habibi, Golnaz How, Jonathan P. |
| author_sort | Shen, Macheng |
| collection | MIT |
| description | © 2018 IEEE. One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection. We first discussed the feature selection for transferable pedestrian motion models in general. Following this discussion, we developed one transferable pedestrian motion prediction algorithm based on Inverse Reinforcement Learning (IRL) that infers pedestrian intentions and predicts future trajectories based on observed trajectory. We evaluated our algorithm at three intersections. We used the accuracy of augmented semi-nonnegative sparse coding (ASNSC), trained and tested at the same intersection as a baseline. The result shows that the proposed algorithm improves the baseline accuracy by a statistically significant percentage in both non-transfer task and transfer task. |
| first_indexed | 2024-09-23T11:45:04Z |
| format | Article |
| id | mit-1721.1/137874.2 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:45:04Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/137874.22021-12-22T17:43:34Z Transferable Pedestrian Motion Prediction Models at Intersections Shen, Macheng Habibi, Golnaz How, Jonathan P. Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Aerospace Controls Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics © 2018 IEEE. One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection. We first discussed the feature selection for transferable pedestrian motion models in general. Following this discussion, we developed one transferable pedestrian motion prediction algorithm based on Inverse Reinforcement Learning (IRL) that infers pedestrian intentions and predicts future trajectories based on observed trajectory. We evaluated our algorithm at three intersections. We used the accuracy of augmented semi-nonnegative sparse coding (ASNSC), trained and tested at the same intersection as a baseline. The result shows that the proposed algorithm improves the baseline accuracy by a statistically significant percentage in both non-transfer task and transfer task. 2021-12-22T17:43:33Z 2021-11-09T14:10:33Z 2021-12-22T17:43:33Z 2019-09 2019-10-28T16:56:31Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137874.2 Shen, Macheng, Habibi, Golnaz and How, Jonathan P. 2019. "Transferable Pedestrian Motion Prediction Models at Intersections." en 10.1109/IROS.2018.8593783 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/octet-stream Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Shen, Macheng Habibi, Golnaz How, Jonathan P. Transferable Pedestrian Motion Prediction Models at Intersections |
| title | Transferable Pedestrian Motion Prediction Models at Intersections |
| title_full | Transferable Pedestrian Motion Prediction Models at Intersections |
| title_fullStr | Transferable Pedestrian Motion Prediction Models at Intersections |
| title_full_unstemmed | Transferable Pedestrian Motion Prediction Models at Intersections |
| title_short | Transferable Pedestrian Motion Prediction Models at Intersections |
| title_sort | transferable pedestrian motion prediction models at intersections |
| url | https://hdl.handle.net/1721.1/137874.2 |
| work_keys_str_mv | AT shenmacheng transferablepedestrianmotionpredictionmodelsatintersections AT habibigolnaz transferablepedestrianmotionpredictionmodelsatintersections AT howjonathanp transferablepedestrianmotionpredictionmodelsatintersections |