Analysis of urban vehicle driving mode based on deep-learning method
DOI: 10.23977/autml.2024.050107 | Downloads: 7 | Views: 116
Author(s)
Su Zhou 1,2, Yang Jiao 1, Jianhua Gao 1, Xiaoman Liu 1
Affiliation(s)
1 College of Automotive Studies, Tongji University, Shanghai, China
2 China-Germany Institute, Tongji University, Shanghai, China
Corresponding Author
Su ZhouABSTRACT
In view of the data processing difficulties in the data collection of vehicle driving behavior, the neural network structures of FCN, ResNet, LSTM and Bi-LSTM are designed and optimized. Then, ResNet and Bi-LSTM with better performance were selected for model fusion, which improved the overall performance of the model. Finally, different degrees of error disturbance are added to the test set to verify the anti-interference and generalization ability of the fusion model. The results show that the classification accuracy of the model does not decline when there is 5% disturbance in the data, maintains a high level of performance, avoids the problem of high-dimensional features after the One-hot coding, realizes the same batch training of variable length samples, and improves the efficiency of model training.
KEYWORDS
Driving behavior recognition, automatic driving, deep learning, neural network, model fusionCITE THIS PAPER
Su Zhou, Yang Jiao, Jianhua Gao, Xiaoman Liu, Analysis of urban vehicle driving mode based on deep-learning method. Automation and Machine Learning (2024) Vol. 5: 50-61. DOI: http://dx.doi.org/10.23977/autml.2024.050107.
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