End-to-end deep learning-based autonomous driving control for high-speed environment

With the recent emergence of artificial intelligence (AI) technology, autonomous vehicle industry has rapidly adopted this technology to investigate self-driving systems based on AI technology. Although autonomous driving is frequently used in high-speed environments, most studies are conducted on low-speed driving on complex urban roads. Currently, most commercialized self-driving cars in SAE autonomous driving level 2 provide practical performance on high-speed roads using various sensors. However, these systems have to process huge sensor data and apply complex control algorithms. Recently, studies have been conducted on the use of image-based end-to-end deep learning to control autonomous driving systems that can be configured at a low cost without expensive sensors and complex processes. In this study, we proposed an autonomous driving control system using a novel end-to-end deep learning model for high-speed environments, and also compared the performance of the proposed system with NVIDIA end-to-end driving system.