Fine-grained highway autonomous vehicle lane-changing trajectory prediction based on a heuristic attention-aided encoder-decoder model
•A novel lane-changing trajectory segmentation and sampling algorithm is proposed.•Heuristic attention-aided encoder-decoder network is developed.•Both vehicle motion state and trajectory in the process of lane-changing are predicted.•Proposed trajectory prediction model can make a fine-grained desc...
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| Vydáno v: | Transportation research. Part C, Emerging technologies Ročník 140; s. 103706 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.07.2022
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| Témata: | |
| ISSN: | 0968-090X, 1879-2359 |
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| Abstract | •A novel lane-changing trajectory segmentation and sampling algorithm is proposed.•Heuristic attention-aided encoder-decoder network is developed.•Both vehicle motion state and trajectory in the process of lane-changing are predicted.•Proposed trajectory prediction model can make a fine-grained description of lane-changing.
Accurate prediction of lane-changing (LC) trajectories can improve traffic efficiency and reduce the probability of accidents, as well as provide driving strategies for intelligent connected vehicles (ICVs) and connected autonomous vehicles (CAVs). Aiming at solving the problems of low prediction accuracy, difficulty in long-term prediction, and an inability of a fine-grained level description of conventional models, a prediction model based on an attention-aided encoder-decoder structure and deep neural network (DNN) is proposed. This study analyzes the LC process and proposes an LC segmentation and sampling method for dividing LC into four stages. The optimal attention-aided encoder-decoder model is tested by trajectory data in the four LC stages and then a heuristic network model is designed. In addition, the proposed heuristic network is connected with the DNN for predicting vehicle kinematics data while predicting the vehicle trajectory. Finally, the heuristic network and DNN are tested in cascade to form a joint cascade prediction model that can perform a fine-grained LC description based on the prediction results. The experimental results show that the proposed cascade prediction model has high accuracy and long-term prediction capability of a vehicle’s trajectory, velocity, acceleration, and steering angle and is also capable of fine-grained LC description. The proposed prediction model can provide a useful theoretical basis for further research on ICVs and CAVs. |
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| AbstractList | •A novel lane-changing trajectory segmentation and sampling algorithm is proposed.•Heuristic attention-aided encoder-decoder network is developed.•Both vehicle motion state and trajectory in the process of lane-changing are predicted.•Proposed trajectory prediction model can make a fine-grained description of lane-changing.
Accurate prediction of lane-changing (LC) trajectories can improve traffic efficiency and reduce the probability of accidents, as well as provide driving strategies for intelligent connected vehicles (ICVs) and connected autonomous vehicles (CAVs). Aiming at solving the problems of low prediction accuracy, difficulty in long-term prediction, and an inability of a fine-grained level description of conventional models, a prediction model based on an attention-aided encoder-decoder structure and deep neural network (DNN) is proposed. This study analyzes the LC process and proposes an LC segmentation and sampling method for dividing LC into four stages. The optimal attention-aided encoder-decoder model is tested by trajectory data in the four LC stages and then a heuristic network model is designed. In addition, the proposed heuristic network is connected with the DNN for predicting vehicle kinematics data while predicting the vehicle trajectory. Finally, the heuristic network and DNN are tested in cascade to form a joint cascade prediction model that can perform a fine-grained LC description based on the prediction results. The experimental results show that the proposed cascade prediction model has high accuracy and long-term prediction capability of a vehicle’s trajectory, velocity, acceleration, and steering angle and is also capable of fine-grained LC description. The proposed prediction model can provide a useful theoretical basis for further research on ICVs and CAVs. |
| ArticleNumber | 103706 |
| Author | Jia, Shuo Wei, Cheng Hui, Fei Yang, Zijiang Khattak, Asad J. |
| Author_xml | – sequence: 1 givenname: Cheng surname: Wei fullname: Wei, Cheng organization: School of Information Engineering, Chang'an University, Xi’an, Shaanxi 710064, China – sequence: 2 givenname: Fei surname: Hui fullname: Hui, Fei email: feihui@chd.edu.cn organization: School of Information Engineering, Chang'an University, Xi’an, Shaanxi 710064, China – sequence: 3 givenname: Zijiang surname: Yang fullname: Yang, Zijiang organization: Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China – sequence: 4 givenname: Shuo surname: Jia fullname: Jia, Shuo organization: School of Information Engineering, Chang'an University, Xi’an, Shaanxi 710064, China – sequence: 5 givenname: Asad J. surname: Khattak fullname: Khattak, Asad J. organization: School of Information Engineering, Chang'an University, Xi’an, Shaanxi 710064, China |
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| Keywords | Lane changing Attention-aided encoder-decoder structure Highway trajectory prediction Heuristic network Fine-grained description |
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