Research on vehicle lane change intention recognition model CNN-BIGRU-ATTENTION optimized by whale algorithm
To address the problem of spatio-temporal diverse features and hyperparameter sensitivity affecting the accuracy of vehicle behavioral intention recognition,this paper proposes an improved CNN-BiGRU-ATTENTION hybrid lane-changing intention recognition model.First,target vehicle trajectory sequences and interaction features with surrounding vehicles are used as model inputs for training to achieve intention recognition prediction considering the dynamic change state of vehicles.Then,a whale optimization algorithm is used to perform multi-objective optimization of model tuning parameters to reduce the difficulty of model tuning.Finally,the model is evaluated and calibrated using the NGSIM dataset. Our results show the accuracy of the proposed WOA-CNN-BiGRU-ATTENTION model is improved by 4.53％ and 0.97％ to 97.64％ compared with the CNN-BiGRU-ATTENTION model and the Transformer model.The WOA-CNN-BiGRU-ATTENTION model with different prediction times achieves the highest accuracy in the intention recognition with the recognition accuracy in the 2.5 s before lane changing exceeding 91％,demonstrating the model performs well in intention recognition of vehicles changing lanes.
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