Deep learning‐based hybrid model for the behaviour prediction of surrounding vehicles over long‐time periods. Issue 10 (3rd June 2022)

• Record Type: Journal Article
• Authors: Gao, Xiang; Chen, Long; Tai, Kangsheng; Cai, Yingfeng; Wang, Hai; Li, Yicheng
• Is Part Of: IET intelligent transport systems. Volume 16:Issue 10(2022)
• Page Start: 1404
• Page End: 1412
• Publication Date: 2022-06-03
• Subjects: Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/itr2.12214
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract: Autonomous vehicles need to have the ability to predict the future behaviour of surrounding vehicles, which helps with proper trajectory planning and tracking. Many behaviour prediction methods have limited application because they have a very limited prediction horizon. This paper proposes a deep learning‐based hybrid model for behaviour prediction over long‐time periods, including manoeuvre recognition and a behaviour prediction module. In manoeuvre recognition module, the CNN extracts the social characteristics of the target vehicle and LSTM outputs the manoeuvre probability vector to form a contextual feature vector with the social features. In the behaviour prediction module, LSTM and Attention are based on the contextual feature vector to capture multi‐time step information in the behaviour time window to complete the prediction of the target vehicle behaviour. 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 behaviour with an accuracy of 89.73% and an average prediction time of 2.032 s, which has a high engineering application value.