考虑驾驶风格的驾驶意图识别模型OA

Accurate recognition of vehicle driving intention plays an important role in ensuring the safety of vehicle operation and enhancing the performance of autonomous vehicles.To address the issue that existing driving intention recognition models fail to fully consider driver diversity,a driving intention recognition model that incorporates the representation of driving styles for both the ego vehicle and surrounding vehicles is proposed.First,a driving behavior matrix(DBM)is constructed based on vehicle trajectory information,with implicit driving style features extracted through a convolutional neural network(CNN),and corresponding weight coefficients are used to measure the influence of surrounding vehicles' driving styles;to reduce redundant information in trajectory data,dimensionality reduction of trajectory information is performed based on interaction factors,and a bi-directional long short-term memory(Bi-LSTM)network is employed to capture the temporal relationships of interaction factors,thereby obtaining inter-vehicle interaction features;finally,inter-vehicle interaction features and driving style features are combined,and driving intention recognition is carried out through a fully connected network.Validation results on the NGSIM(next generation simulation)dataset show that the proposed model achieves a driving intention recognition accuracy of 98.11％,demonstrating better recognition performance compared to existing driving intention recognition models.