• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2022, Vol. 58 ›› Issue (22): 369-378.doi: 10.3901/JME.2022.22.369

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Lane Change Intention Prediction of CNN-LSTM Based on Multi-head Attention

GAO Kai1,2, LI Xun-hao1, HU Lin1, CHEN Bin1, DU Rong-hua1,2   

  1. 1. College of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114;
    2. Hunan Key Laboratory of Smart Roadwasy and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114
  • Received:2021-11-30 Revised:2022-05-20 Online:2022-11-20 Published:2023-02-07

Abstract: In the mixed traffic environment of autonomous vehicles and traditional vehicles, the prediction of vehicle lane change intention can provide an effective guarantee for the safe driving of autonomous vehicles. In order to more accurately predict the vehicle lane change intention, a novel vehicle lane change intention prediction algorithm is proposed by combining the multi head attention with convolution neural network(CNN) and long-short term memory(LSTM) network. Firstly, the NGSIM(Next generation simulaion) data set is processed to extract the vehicle lateral position information and surrounding environment information. Then, it is input into the CNN-LSTM model based on multi-head attention to improve the feature extraction ability and prediction accuracy of the input sequence. Finally, the effectiveness of the proposed model is verified on the NGSIM dataset. The experimental results show that the model can extract important features from a large number of data. At the same time, through the feature comparison experiment, it is found that the lateral position information is the main feature of prediction, and the surrounding environment information is the auxiliary feature of prediction. Finally, through the comparison experiment of the model, it is concluded that the prediction accuracy of the model is better than that of LSTM, CNN and CNN-LSTM models before 1s, 2s and 3s. It can provide help and reference for the design of advanced prediction algorithms for autopilot cars.

Key words: autonomous driving, lane change intention, CNN, LSTM, multi-head attention

CLC Number: