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Original Article
CAEV- Deep Learning-Based Hybrid Model for the Behavior Prediction of Surrounding Vehicles Over Long-time Periods
Kangsheng Tai
Jiangsu University
Corresponding Author
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.
Keywords
Autonomous vehicles, Behavior prediction, Deep learning, Long-time periods, Hybrid model
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 07 Jun, 2020
No community comments so far
Editorial decision: Major revision
On 22 Dec, 2020
Review #2 received
Received 19 Oct, 2020
Reviewer #2 agreed
On 30 Sep, 2020
Review #1 received
Received 31 Jul, 2020
Reviewer #1 agreed
On 08 Jul, 2020
Reviewers invited
Invitations sent on 01 Jul, 2020
Editor assigned
On 03 Jun, 2020
First submitted
On 02 Jun, 2020
Submission checks complete
On 02 Jun, 2020
Editor invited
On 02 Jun, 2020
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Subject Areas
Mechanical Engineering
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This preprint is under consideration at Chinese Journal of Mechanical Engineering. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Original Article
CAEV- Deep Learning-Based Hybrid Model for the Behavior Prediction of Surrounding Vehicles Over Long-time Periods
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
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 07 Jun, 2020
No community comments so far
Editorial decision: Major revision
On 22 Dec, 2020
Review #2 received
Received 19 Oct, 2020
Reviewer #2 agreed
On 30 Sep, 2020
Review #1 received
Received 31 Jul, 2020
Reviewer #1 agreed
On 08 Jul, 2020
Reviewers invited
Invitations sent on 01 Jul, 2020
Editor assigned
On 03 Jun, 2020
First submitted
On 02 Jun, 2020
Submission checks complete
On 02 Jun, 2020
Editor invited
On 02 Jun, 2020
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
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.