Enhancing lane changing trajectory prediction on highways: A heuristic attention-based encoder-decoder model
Accurate prediction of lane changing (LC) trajectories plays a vital role in ensuring safe and efficient traffic flow on highways. This paper proposes a LC trajectory prediction model based on encoder-decoder architecture to address low long-term prediction accuracy problem and to gain insight into...
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| Vydáno v: | Physica A Ročník 639; s. 129696 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.04.2024
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| ISSN: | 0378-4371, 1873-2119 |
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| Abstract | Accurate prediction of lane changing (LC) trajectories plays a vital role in ensuring safe and efficient traffic flow on highways. This paper proposes a LC trajectory prediction model based on encoder-decoder architecture to address low long-term prediction accuracy problem and to gain insight into the underlying motivations of LC behavior. Three specific enhancements were proposed to improve the performance of encoder-decoder. The first enhancement involves the utilization of Neighborhood Component Analysis (NCA) to identify the most relevant input features from driving environment. The second enhancement involves the introduction of heuristic attention to capture intricate time-dependent and environment-dependent patterns of input data. Finally, a trajectory check module was proposed to supervise and adjust the prediction outputs based on vehicle dynamics constraints. The proposed model was trained and evaluated using the HighD dataset. Experimental results demonstrated that the proposed model outperforms improved encoder-decoder architectures designed by previous studies in terms of prediction accuracy in long-term prediction, which the lateral and longitude MAE were 0.041 m and 1.842 m in 5 s prediction duration, respectively. Additionally, it was observed that preceding vehicles in the current and target lane, and following vehicle in the target lane, exert a significantly influential effect on LC maneuvers, which represents the underlying motivations behind LC behavior. The findings of this study contribute to the advancement of intelligent transportation systems (ITS) and autonomous vehicles (AVs) by providing an effective LC trajectory prediction.
•Neighborhood Component Analysis is employed to identifying and analyzing crucial features and motivations of LC behavior.•An encoder-decoder architecture coupled with time-dependent and environment-dependent attention mechanism can predict the future trajectory accurately in lateral and longitudinal direction.•Trajectory check module can guarantee the rationality of prediction outputs and adjust those unreasonable prediction. |
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| AbstractList | Accurate prediction of lane changing (LC) trajectories plays a vital role in ensuring safe and efficient traffic flow on highways. This paper proposes a LC trajectory prediction model based on encoder-decoder architecture to address low long-term prediction accuracy problem and to gain insight into the underlying motivations of LC behavior. Three specific enhancements were proposed to improve the performance of encoder-decoder. The first enhancement involves the utilization of Neighborhood Component Analysis (NCA) to identify the most relevant input features from driving environment. The second enhancement involves the introduction of heuristic attention to capture intricate time-dependent and environment-dependent patterns of input data. Finally, a trajectory check module was proposed to supervise and adjust the prediction outputs based on vehicle dynamics constraints. The proposed model was trained and evaluated using the HighD dataset. Experimental results demonstrated that the proposed model outperforms improved encoder-decoder architectures designed by previous studies in terms of prediction accuracy in long-term prediction, which the lateral and longitude MAE were 0.041 m and 1.842 m in 5 s prediction duration, respectively. Additionally, it was observed that preceding vehicles in the current and target lane, and following vehicle in the target lane, exert a significantly influential effect on LC maneuvers, which represents the underlying motivations behind LC behavior. The findings of this study contribute to the advancement of intelligent transportation systems (ITS) and autonomous vehicles (AVs) by providing an effective LC trajectory prediction.
•Neighborhood Component Analysis is employed to identifying and analyzing crucial features and motivations of LC behavior.•An encoder-decoder architecture coupled with time-dependent and environment-dependent attention mechanism can predict the future trajectory accurately in lateral and longitudinal direction.•Trajectory check module can guarantee the rationality of prediction outputs and adjust those unreasonable prediction. |
| ArticleNumber | 129696 |
| Author | Bo, Peng Chen, Yili Li, Keping Chen, Yingda Xiao, Xue |
| Author_xml | – sequence: 1 givenname: Xue surname: Xiao fullname: Xiao, Xue organization: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China – sequence: 2 givenname: Peng surname: Bo fullname: Bo, Peng organization: Transportation College, Jilin University, Changchun 130012, China – sequence: 3 givenname: Yingda surname: Chen fullname: Chen, Yingda email: chenyd@tongji.edu.cn organization: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China – sequence: 4 givenname: Yili surname: Chen fullname: Chen, Yili organization: PTV Software Technology (Shanghai) Co., Ltd., Shanghai 200001, China – sequence: 5 givenname: Keping surname: Li fullname: Li, Keping organization: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China |
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| CitedBy_id | crossref_primary_10_1016_j_physa_2024_130158 crossref_primary_10_1109_TITS_2024_3458439 |
| Cites_doi | 10.1145/2347736.2347755 10.1109/MITS.2021.3049404 10.1177/0278364920917446 10.1109/TITS.2014.2331758 10.1109/ACCESS.2019.2907000 10.1177/03611981211028624 10.1016/j.trc.2019.07.002 10.1109/TVT.2021.3076473 10.1162/neco.1997.9.8.1735 10.1016/j.trc.2018.06.007 10.1109/LRA.2020.2974393 10.1016/j.apergo.2015.03.017 10.1109/TITS.2015.2498841 10.1016/j.trc.2019.05.021 10.1007/BF00114844 10.1109/TIV.2022.3167103 10.1016/j.trc.2018.01.016 |
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| Keywords | Highway environment with heterogeneous traffic flow Lane changing motivations Lane changing trajectory prediction Heuristic attention-based encoder-decoder model |
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| SubjectTerms | Heuristic attention-based encoder-decoder model Highway environment with heterogeneous traffic flow Lane changing motivations Lane changing trajectory prediction |
| Title | Enhancing lane changing trajectory prediction on highways: A heuristic attention-based encoder-decoder model |
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