As the interest of the autonomous driving increases, techniques related to the advanced driver assistance system are evolving together. In this paper, we propose a novel driver identification system using convolutional neural network (CNN) with the background removal-based infrared image data augmentation. It helps to identify who a driver is, and provides the customized driving environment. The process for the proposed identification system is as follows. First, we acquire customized individual infrared images in a driving simulation environment. Second, we augment the large amount of data by using the background removal-based method and several image processing techniques. Third, the augmented data is trained by the low-complexity-based CNN method. Finally, we load all trained weights to the forward network for real-time processing. In the experimental results, the proposed system had the memory resource of 4,795 KB, which are up to 49.0822 times smaller than benchmark algorithms, and the average F1 score of 0.9418 for the driver identification accuracy.
Driver Identification System Using Convolutional Neural Network with Background Removal-based Infrared Data Augmentation
2018 IEEE Intelligent Vehicles Symposium (IV) ; 1989-1994
2018-06-01
1668080 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
BASE | 2018
|Driver identification using 1D convolutional neural networks with vehicular CAN signals
IET | 2021
|