ETRI Journal

  • 제45권1호
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  • Pages.75-92
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  • 2023
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Study on the Take-over Performance of Level 3 Autonomous Vehicles Based on Subjective Driving Tendency Questionnaires and Machine Learning Methods

  • Hyunsuk Kim (Cognition & Transportation ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Woojin Kim (Cognition & Transportation ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Jungsook Kim (Cognition & Transportation ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Seung-Jun Lee (Cognition & Transportation ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Daesub Yoon (Cognition & Transportation ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Oh-Cheon Kwon (Cognition & Transportation ICT Research Section, Electronics and Telecommunications Research Institute) ;
  • Cheong Hee Park (Department of Computer Science and Engineering, Chungnam National University)
  • 투고 : 2021.07.13
  • 심사 : 2022.01.16
  • 발행 : 2023.02.20
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초록

Level 3 autonomous vehicles require conditional autonomous driving in which autonomous and manual driving are alternately performed; whether the driver can resume manual driving within a limited time should be examined. This study investigates whether the demographics and subjective driving tendencies of drivers affect the take-over performance. We measured and analyzed the reengagement and stabilization time after a take-over request from the autonomous driving system to manual driving using a vehicle simulator that supports the driver's take-over mechanism. We discovered that the driver's reengagement and stabilization time correlated with the speeding and wild driving tendency as well as driving workload questionnaires. To verify the efficiency of subjective questionnaire information, we tested whether the driver with slow or fast reengagement and stabilization time can be detected based on machine learning techniques and obtained results. We expect to apply these results to training programs for autonomous vehicles' users and personalized human-vehicle interfaces for future autonomous vehicles.

키워드

과제정보

We thank Hyung-ki Kim of Neighbor System Co. Ltd. for helping us to prepare. This work was supported by the Transportation and Logistics R&D Program of the Ministry of Land, Infrastructure, and Transport, Republic of Korea (20TLRPB131486-04, Autonomous driving vehicle [SAE Level 2,3] based human factor in-depth study).

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