An Intelligent Driving Assistance System Based on Lightweight Deep Learning Models
Ko-Feng Lee, Xiu-Zhi Chen, Chao-Wei Yu, Kai-Yi Chin, Yih-Chen Wang, Chia-Yu Hsiao, Yen‐Lin Chen
Abstract
An intelligent driver assistance system is developed in this study, which is able to remind the drivers to turn on the head lights or wipers through situation recognition method when driving at night or on rainy days. Furthermore, it is also able to alarm the drivers based on the lightweight deep learning model and the distance estimation method when surrounding vehicles are too close. Experimental results show that the proposed methods and the chosen lightweight model in our proposed system obtain reliable performance and sufficient computational efficiency under limited computing resource. In conclude, our proposed system obtains high probability to be adopted for the development of advanced driver assistance systems (ADAS).