Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning.
This study proposes a Driving style-Tri Channel Trajectory Model (DS-TCTM) to enhance vehicle trajectory prediction accuracy and driving safety. The framework operates through three rigorously designed stages: (1)Data preprocessing involving kinematics feature extraction, (2)Driving style recognitio...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326937 |
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| author | Jinhao Yang Junwen Cao Mingyu Fang |
| author_facet | Jinhao Yang Junwen Cao Mingyu Fang |
| author_sort | Jinhao Yang |
| collection | DOAJ |
| description | This study proposes a Driving style-Tri Channel Trajectory Model (DS-TCTM) to enhance vehicle trajectory prediction accuracy and driving safety. The framework operates through three rigorously designed stages: (1)Data preprocessing involving kinematics feature extraction, (2)Driving style recognition utilizing acceleration variation rate and average time headway combined with K-Means++ traffic density clustering and K-neighbor Gaussian mixture model (K-GMM) analysis to classify driving behaviors into conservative, moderate, and radical categories, and (3)Personalized trajectory prediction employing a multi-level neural architecture with dedicated sub-networks for distinct driving styles. Experimental evaluations demonstrate DS-TCTM's superior performance across multiple dimensions. The model achieves a mean RMSE of 4.46 and NLL of 3.89 across varying prediction horizons, with 35.8% error reduction attained after 100 hyperparameter optimization iterations. Comparative analysis with baseline models (LSTM, Social-LSTM, Social-Velocity-LSTM, Convolutional-Social-LSTM) reveals particularly enhanced accuracy in long-term predictions. These results confirm DS-TCTM's effectiveness in capturing driving style impacts on trajectory patterns, providing reliable prediction enhancements for vehicle safety systems. This methodology advances personalized trajectory modeling with practical intelligent transportation applications. |
| format | Article |
| id | doaj-art-231f44c7543b4e15a6ef54cbb6acd353 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-231f44c7543b4e15a6ef54cbb6acd3532025-08-20T03:50:52ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032693710.1371/journal.pone.0326937Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning.Jinhao YangJunwen CaoMingyu FangThis study proposes a Driving style-Tri Channel Trajectory Model (DS-TCTM) to enhance vehicle trajectory prediction accuracy and driving safety. The framework operates through three rigorously designed stages: (1)Data preprocessing involving kinematics feature extraction, (2)Driving style recognition utilizing acceleration variation rate and average time headway combined with K-Means++ traffic density clustering and K-neighbor Gaussian mixture model (K-GMM) analysis to classify driving behaviors into conservative, moderate, and radical categories, and (3)Personalized trajectory prediction employing a multi-level neural architecture with dedicated sub-networks for distinct driving styles. Experimental evaluations demonstrate DS-TCTM's superior performance across multiple dimensions. The model achieves a mean RMSE of 4.46 and NLL of 3.89 across varying prediction horizons, with 35.8% error reduction attained after 100 hyperparameter optimization iterations. Comparative analysis with baseline models (LSTM, Social-LSTM, Social-Velocity-LSTM, Convolutional-Social-LSTM) reveals particularly enhanced accuracy in long-term predictions. These results confirm DS-TCTM's effectiveness in capturing driving style impacts on trajectory patterns, providing reliable prediction enhancements for vehicle safety systems. This methodology advances personalized trajectory modeling with practical intelligent transportation applications.https://doi.org/10.1371/journal.pone.0326937 |
| spellingShingle | Jinhao Yang Junwen Cao Mingyu Fang Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. PLoS ONE |
| title | Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. |
| title_full | Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. |
| title_fullStr | Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. |
| title_full_unstemmed | Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. |
| title_short | Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. |
| title_sort | personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning |
| url | https://doi.org/10.1371/journal.pone.0326937 |
| work_keys_str_mv | AT jinhaoyang personalizedtrajectoryinferenceframeworkintegratingdrivingbehaviorrecognitionandtemporaldependencylearning AT junwencao personalizedtrajectoryinferenceframeworkintegratingdrivingbehaviorrecognitionandtemporaldependencylearning AT mingyufang personalizedtrajectoryinferenceframeworkintegratingdrivingbehaviorrecognitionandtemporaldependencylearning |