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Analysis of Take-over Time and Stabilization of Autonomous Vehicle Using a Driving Simulator

드라이빙 시뮬레이터를 이용한 자율주행자동차 제어권 전환 소요시간 및 안정화 특성 분석

  • Park, Sungho (Dept. of Construction and Transportation Eng., Ajou University) ;
  • Jeong, Harim (Dept. of Construction and Transportation Eng., Ajou University) ;
  • Kwon, Cheolwoo (Dept. of Construction and Transportation Eng., Ajou University) ;
  • Kim, Jonghwa (Korea Automobile Testing & Research Institute, Korea Transportation Safety Authority) ;
  • Yun, Ilsoo (Dept. of Transportation Eng., Ajou University)
  • 박성호 (아주대학교 건설교통공학과) ;
  • 정하림 (아주대학교 건설교통공학과) ;
  • 권철우 (아주대학교 건설교통공학과) ;
  • 김종화 (한국교통안전공단 자동차안전연구원 자율주행실 자율주행정책처) ;
  • 윤일수 (아주대학교 교통시스템공학과)
  • Received : 2019.02.14
  • Accepted : 2019.07.31
  • Published : 2019.08.31

Abstract

Take-overs occur in autonomous vehicles at levels 3 and 4 based on SAE. For safe take-over, it is necessary to set the time required for diverse drivers to complete take-over in various road conditions. In this study, take-over time and stabilization characteristics were measured to secure safety of take-over in autonomous vehicle. To this end, a virtual driving simulator was used to set up situations similar to those on real expressways. Fifty drivers with various sexes and ages participated in the experiment where changes in traffic volume and geometry were applied to measure change in takeover time and stabilization characteristics according to various road conditions. Experimental results show that the average take-over time was 2.3 seconds and the standard deviation was 0.1 second. As a result of analysis of stabilization characteristics, there was no difference in take-over stabilization time due to the difference of traffic volume, and there was a significant difference by curvature changes.

SAE 기준 3단계의 자율주행자동차에서는 필요 시 운전의 주체가 시스템에서 운전자로 또는 그 반대로 이전되는 제어권 전환(take-over)이 발생하게 된다. 이때 안전한 제어권 전환을 위해서는 다양한 도로환경에서 여러 계층의 운전자들이 안전하게 제어권 전환을 완료하는 데 필요한 시간을 설정하는 것이 중요하다. 본 연구에서는 자율주행자동차의 제어권 전환의 안전성을 확보하기 위해 제어권 전환 소요시간 및 안정화 특성을 분석하였다. 이를 위해 드라이빙 시뮬레이터를 활용하였으며, 고속도로와 유사한 상황을 설정하여 실험을 진행하였다. 다양한 성별 및 나이를 가진 50명의 운전자가 실험에 참가하였고, 각 피실험자별로 교통량과 기하구조의 변화를 주어서 다양한 상황에 따른 제어권 전환 소요시간 변화와 안정화 특성을 측정하였다. 실험 결과, 제어권 전환 소요시간은 평균 2.3초였으며 표준편차는 0.1초로 분석되었다. 또한 안정화 특성 분석 결과, 고속도로 교통량 차이에 따른 제어권 전환 안정화 시간은 차이는 없었으며, 곡선반경의 변화에 따라서는 유의미한 차이가 있는 것으로 나타났다.

Keywords

References

  1. Clark H. and Feng J.(2017), "Age differences in the takeover of vehicle control and engagement in non-driving-related activities in simulated driving with conditional automation," Accident Analysis & Prevention, vol. 106, pp.468-479. https://doi.org/10.1016/j.aap.2016.08.027
  2. Gold C., Korber M., Lechner D. and Bengler K.(2016), "Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations: The Role of Traffic Density," Human Factors, vol. 58, no. 4, pp.642-652. https://doi.org/10.1177/0018720816634226
  3. Hergeth S., Lorenz L. and Krems J. F.(2017), "Prior Familiarization With Takeover Requests Affects Drivers' Takeover Performance and Automation Trust," Human Factors, vol. 59, no. 3, pp.457-470. https://doi.org/10.1177/0018720816678714
  4. Jeong Y., Park H. S., Kim B. H. and Kim Y.(2013), "Combined Filtering Model Using Voting Rule and Median Absolute Deviation for Travel Time Estimation," The Journal of The Korea Institute of Intelligent Transport Systems, vol. 12, no. 6, pp.10-21. https://doi.org/10.12815/kits.2013.12.6.010
  5. Kang Y. K.(2011), Exploration of Optimal Statistical Methods to Analyze Longitudinal Data with Missing Values for Clinical Research Involving Knee Arthroplasty, Dankook University.
  6. Kim H. J. and Yang J. H.(2017), "Takeover Requests in Simulated Partially Autonomous Vehicles Considering Human Factors," IEEE Transactions on Human-Machine Systems, vol. 47, no. 5, pp.735-740. https://doi.org/10.1109/THMS.2017.2674998
  7. Kim N., Yang M., Lee J. and Kim J.(2018), "A study on the effect of information types on Drivers in Takeover period of automated vehicles," Journal of Digital Contents Society, vol. 19, no. 1, pp.113-122. https://doi.org/10.9728/dcs.2018.19.1.113
  8. Kim Y. Y., Kim E. N., Jung C. Y., Go H. D. and Kim H. Y.(2002), "The Efficacy of Biofeedback in Reducing Cybersickness in Virtual Navigation," Science of Emotion & Sensibility, vol. 5, no. 2, pp.29-34.
  9. Lim D. S.(2016), Efficiency Evaluation of Prolonged Green Time Durations at Signalized Intersections, Kyonggi University.
  10. McCauley M. E. and Sharkey T. J.(1992), "Cybersickness: Perception of self-motion in virtual environments," Presence: Teleoperators and Virtual environments, vol. 1, no. 3, pp.311-318. https://doi.org/10.1162/pres.1992.1.3.311
  11. Ministry of Land, Infrastructure and Transport(2013), Highway Capacity Manual.
  12. Ministry of Land, Infrastructure and Transport(2013), Road structures and facilities Commentary Guidelines.
  13. National Highway Traffic Safety Administration(2017), Federal Automated Vehicle Policy.
  14. Roche F. and Brandenburg S.(2018), "Should the urgency of auditory-tactile takeover requests match the criticality of takeover situations?," 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp.1035-1040.
  15. Son J. W.(2017), A Study on The Application Status of Cyber Sickness Mitigation Methods in Korean Virtual Reality Games, Sangmyung University.
  16. Yoon S. H., Kim Y. W. and Ji Y. G.(2019), "The effects of takeover request modalities on highly automated car control transitions," Accident Analysis & Prevention, vol. 123, pp.150-158. https://doi.org/10.1016/j.aap.2018.11.018