Abstract
One of the biggest advancements in automotive technology that is revolutionizing transportation is autonomous driving. According to international standards, autonomous driving is categorized into levels 0 through 5. Currently, South Korea has commercialized Level 2 and is waiting for Level 3. Level 3 is the stage where the system controls everything under certain conditions and the driver intervenes in emergency situations. In autonomous driving, accidents due to system problems can occur at the moment when the driver does not intervene, and the manufacturer is responsible for them. Event Data Recorder and Data Storage System of Automated Driving can identify the cause of the accident, but the records are only visible to the manufacturer, making it difficult for consumers to access the information. This research aims to develop a CAN-based device that can accurately identify the driving mode and increase the safety and reliability of the vehicle by developing a network inside the car. The proposed system will improve the accuracy of driving mode identification and help solve the liability issue between consumers and manufacturers in the event of an accident.
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Acknowledgements
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-01197, Convergence security core talent training business (SoonChunHyangUniversity)).
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Yu, K., Ryu, D., Jeong, M., Batzorig, M., Yim, K. (2024). CAN-Based Identification of Human and Autonomous Driving Modes in Level 3 Autonomous Vehicles. In: Barolli, L. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-031-72322-3_8
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DOI: https://doi.org/10.1007/978-3-031-72322-3_8
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