Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies
mode - bus, place - asia, place - urban, planning - safety/accidents, planning - methods, technology - intelligent transport systems
Autonomous bus operation, risk identification, image recognition, AlexNet, data augmentation strategies
The autonomous bus is a key application scenario for autonomous driving technology. Identifying the risk of autonomous bus operation is of great significant to improve road traffic safety and promote the large-scale application of autonomous driving technology.
For the purpose of risk identification, the actual operation data for autonomous buses in Shanghai were converted into 3 kinds of grayscale images and 1 kind of radar image from a temporal–spatial perspective, and a deep learning convolutional neural network, AlexNet, was applied for image recognition. This article uses several image data augmentation strategies to address the problem of uneven distribution of samples and compares the effectiveness of different strategies.
The optimal accuracy (ACC) of the risk identification was 90.4%, the optimal true positive rate (TPR) was 83.7%, and the optimal false negative rate (FPR) was 94.58%. The accuracy of risk identification using AlexNet was higher based on the sample containing Fourier images. In addition, risk identification accuracy based on grayscale images was higher than that based on radar images.
Autonomous buses were found to be vulnerable to risks in areas such as turning sections and intersections. In addition, the results show that the Fourier transform, an image augmentation strategy, can effectively solve the uneven sample distribution problem, and the length of the input time series has an impact on the accuracy of the risk identification.
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Cheng, S., & Wang, J. (2023). Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies. Traffic Injury Prevention, Vol. 24(3), pp. 232-236.