The Predictive Diagnosis Method of Electric Drive State Via an Artificial Neural Network

Safonov, Y.M.; Fedortsov, N.N.; Dulnev, N.N.; Blagodarov, D.A.

doi:10.1109/uralcon52005.2021.9559510

Cited by 2 publications

(1 citation statement)

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“…The neural network-based driving behavior modeling can extract the driving behavior features from the pre-training model and then train the driving behavior model. The authors in [14] designed a threelayer feedforward neural network to extract driver behavior features and used real driving behavior data to achieve autonomous driving of vehicles. The authors in [15] improved the three-layer into a multi-layer, designed a multi-layer feedforward neural network, and built a driving behavior neural network model with the relevant nonlinear function approximation, which improved the convergence speed; however, the prediction results were not satisfactory when the size of the training data was small.…”

Section: Related Workmentioning

confidence: 99%

Driving Violation Prediction Based on an Emotional Style Transfer Network

Wang

2023

Sustainability

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Emotions are closely related to driving behavior, and drivers with different emotions have different degrees of bad driving behavior. In order to explore the relationship between emotions and driving violations, a prediction model based on an emotional style transfer network is proposed. First, inspired by the idea of generative adversarial networks (GAN), the eigenvalues of emotions are extracted. Secondly, the one-way propagation method of the GAN network is improved to cyclic generation, which avoids the problems of non-convergence and long periods in the data training process, improving the utilization of training data. Thirdly, a driving violation prediction model is designed. In this model, the emotion factors are designed as time-related sequences, and by improving the Long Short-Term Memory (LSTM) model, the encoding and decoding processes of the time-related sequences are added to form the context, which improves the accuracy of prediction. Finally, the experimental and simulation data show that the proposed model has significant advantages in loss value, accuracy rate, macro-average score, and other indicators. At the same time, an emotion-induction scheme is given to reduce the possibility of driving violations. Furthermore, the proposed model can provide a theoretical basis for the impact of emotions on driving safety.

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Section: Related Workmentioning

confidence: 99%