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Original Article
Kangsheng Tai
Jiangsu University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Autonomous vehicles need to have the ability to predict the future behavior of surrounding vehicles, which helps with proper trajectory planning and tracking. Many behavior prediction methods have limited application because they have a very limited prediction horizon. This paper proposes a deep learning-based hybrid model for behavior prediction over long-time periods, including maneuver recognition and a behavior prediction module. In the previous module, the CNN extracts the social characteristics of the target vehicle and LSTM outputs the maneuver probability vector and forms a contextual feature vector with the social features. In the lateral module, LSTM and Attention are based on the contextual feature vector to capture multi-time step information in the behavior time window to complete the prediction of the target vehicle behavior. Real-car collection and open-source vehicle trajectory datasets were used for training and testing. The results show that the proposed algorithm could predict vehicle behavior with an accuracy of 89.73% and an average prediction time of 2.032 s, which has a high engineering application value.
Keywords
Autonomous vehicles, Behavior prediction, Deep learning, Long-time periods, Hybrid model
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This is a preprint. It has not completed peer review.
Original Article
Kangsheng Tai
Jiangsu University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 07 Jun, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Autonomous vehicles need to have the ability to predict the future behavior of surrounding vehicles, which helps with proper trajectory planning and tracking. Many behavior prediction methods have limited application because they have a very limited prediction horizon. This paper proposes a deep learning-based hybrid model for behavior prediction over long-time periods, including maneuver recognition and a behavior prediction module. In the previous module, the CNN extracts the social characteristics of the target vehicle and LSTM outputs the maneuver probability vector and forms a contextual feature vector with the social features. In the lateral module, LSTM and Attention are based on the contextual feature vector to capture multi-time step information in the behavior time window to complete the prediction of the target vehicle behavior. Real-car collection and open-source vehicle trajectory datasets were used for training and testing. The results show that the proposed algorithm could predict vehicle behavior with an accuracy of 89.73% and an average prediction time of 2.032 s, which has a high engineering application value.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This preprint is available for download as a PDF.
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