CAEV- Deep Learning-Based Hybrid Model for the Behavior Prediction of Surrounding Vehicles Over Long-time Periods
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.
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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.
Posted 07 Jun, 2020
On 22 Dec, 2020
Received 19 Oct, 2020
On 30 Sep, 2020
Received 31 Jul, 2020
On 08 Jul, 2020
Invitations sent on 01 Jul, 2020
On 03 Jun, 2020
On 02 Jun, 2020
On 02 Jun, 2020
On 02 Jun, 2020
CAEV- Deep Learning-Based Hybrid Model for the Behavior Prediction of Surrounding Vehicles Over Long-time Periods
Posted 07 Jun, 2020
On 22 Dec, 2020
Received 19 Oct, 2020
On 30 Sep, 2020
Received 31 Jul, 2020
On 08 Jul, 2020
Invitations sent on 01 Jul, 2020
On 03 Jun, 2020
On 02 Jun, 2020
On 02 Jun, 2020
On 02 Jun, 2020
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.