Abstract: Intelligent transport system (ITS) is the development direction of future transport systems, in which intelligent vehicles are the key components. In order to protect the safety of intelligent vehicles, machine learning techniques are widely used in ITS. For intelligent protection in ITS, the study introduces an improved driving behaviour modelling method based on Bagging Gaussian Process Regression. Meanwhile, to further promote the accuracy of driving behaviour modelling and prediction, Convolutional Neural Network-Long and Short-term Memory Network-Gaussian Process Regression are used for effective feature extraction. The results show that in the straight overtaking scenario, the mean absolute error, root mean square error and maximum absolute error of the improved Bagging Gaussian process regression method are 0.5241, 0.9547 and 10.7705, respectively. In the corner obstacle avoidance scenario, the improved Bagging Gaussian process regression method is only 0.6527, 0.9436 and 14.7531. Besides, the mean absolute error of the Convolutional Neural Network-Long and Short-term Memory Network-Gaussian process regression algorithm is only 0.0387 in the case of the input temporal image frame number of 5. This denoted that the method put forward in the study can provide a more accurate and robust modeling and prediction of driving behaviours in complex traffic environments, and it has a high application potential in the field of safety and protection of intelligent vehicles.

Yuan Chen, “Machine Learning Techniques for Protecting Intelligent Vehicles in Intelligent Transport Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150826

@article{Chen2024,
title = {Machine Learning Techniques for Protecting Intelligent Vehicles in Intelligent Transport Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150826},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150826},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Yuan Chen}
}