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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2212.10737 |
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| _version_ | 1866913214111940608 |
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| author | Xu, Tu Wu, Kan Zhu, Yongdong Ji, Wei |
| author_facet | Xu, Tu Wu, Kan Zhu, Yongdong Ji, Wei |
| contents | This paper proposes a new driving style recognition approach that allows autonomous vehicles (AVs) to perform trajectory predictions for surrounding vehicles with minimal data. Toward that end, we use a hybrid of offline and online methods in the proposed approach. We first learn typical driving styles with PCA and K-means algorithms in the offline part. After that, local Maximum-Likelihood techniques are used to perform online driving style recognition. We benchmarked our method on a real driving dataset against other methods in terms of the RMSE value of the predicted trajectory and the observed trajectory over a 5s duration. The proposed approach can reduce trajectory prediction error by up to 37.7\% compared to using the parameters from other literature and up to 24.4\% compared to not performing driving style recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_10737 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Driving Style Recognition at First Impression for Online Trajectory Prediction Xu, Tu Wu, Kan Zhu, Yongdong Ji, Wei Systems and Control This paper proposes a new driving style recognition approach that allows autonomous vehicles (AVs) to perform trajectory predictions for surrounding vehicles with minimal data. Toward that end, we use a hybrid of offline and online methods in the proposed approach. We first learn typical driving styles with PCA and K-means algorithms in the offline part. After that, local Maximum-Likelihood techniques are used to perform online driving style recognition. We benchmarked our method on a real driving dataset against other methods in terms of the RMSE value of the predicted trajectory and the observed trajectory over a 5s duration. The proposed approach can reduce trajectory prediction error by up to 37.7\% compared to using the parameters from other literature and up to 24.4\% compared to not performing driving style recognition. |
| title | Driving Style Recognition at First Impression for Online Trajectory Prediction |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2212.10737 |