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

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Abstract

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.

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Acknowledgements

This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2016-0-00465) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation).

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  1. Konkuk University, Seoul, South Korea

    Cheol-jin Kim, Myung-jae Lee, Kyu-hong Hwang & Young-guk Ha

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  1. Cheol-jin Kim
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  2. Myung-jae Lee
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  3. Kyu-hong Hwang
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  4. Young-guk Ha
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Correspondence to Young-guk Ha.

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Kim, Cj., Lee, Mj., Hwang, Kh. et al. End-to-end deep learning-based autonomous driving control for high-speed environment. J Supercomput 78, 1961–1982 (2022). https://doi.org/10.1007/s11227-021-03929-8

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  • DOI: https://doi.org/10.1007/s11227-021-03929-8

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