DOI QR코드

DOI QR Code

Selection of Evaluation Metrics for Grading Autonomous Driving Car Judgment Abilities Based on Driving Simulator

드라이빙 시뮬레이터 기반 자율주행차 판단능력 등급화를 위한 평가지표 선정

  • Received : 2023.11.16
  • Accepted : 2023.12.18
  • Published : 2024.02.01

Abstract

Autonomous vehicles at Levels 3 to 5, currently under global research and development, seek to replace the driver's perception, judgment, and control processes with various sensors integrated into the vehicle. This integration enables artificial intelligence to autonomously perform the majority of driving tasks. However, autonomous vehicles currently obtain temporary driving permits, allowing them to operate on roads if they meet minimum criteria for autonomous judgment abilities set by individual countries. When autonomous vehicles become more widespread in the future, it is anticipated that buyers may not have high confidence in the ability of these vehicles to avoid hazardous situations due to the limitations of temporary driving permits. In this study, we propose a method for grading the judgment abilities of autonomous vehicles based on a driving simulator experiment comparing and evaluating drivers' abilities to avoid hazardous situations. The goal is to derive evaluation criteria that allow for grading based on specific scenarios and to propose a framework for grading autonomous vehicles. Thirty adults (25 males and 5 females) participated in the driving simulator experiment. The analysis of the experimental results involved K-means cluster analysis and independent sample t-tests, confirming the possibility of classifying the judgment abilities of autonomous vehicles and the statistical significance of such classifications. Enhancing confidence in the risk-avoidance capabilities of autonomous vehicles in future hazardous situations could be a significant contribution of this research.

현재 전 세계적으로 연구·개발 중인 자율주행차 Level 3에서 Level 5단계는 운전자의 인지-판단-제어과정을 차량에 탑재된 각종 센서로 대체하여, 운전과정의 대부분을 인공지능이 자율적으로 수행할 수 있도록 한다. 하지만 현재 자율주행차는 국가별로 상이한 자율주행차의 판단능력 최소기준을 만족할 경우, 임시운행 허가를 받아 도로주행이 가능하도록 하고 있다. 향후 자율주행차가 보급될 때 구매자들은 임시운행 허가의 한계로 위험상황 회피능력에 대한 신뢰도가 높지 않을 것으로 예상된다. 이에 본 연구에서는 드라이빙 시뮬레이터 기반으로 운전자의 위험상황 회피능력 비교·평가를 통해 자율주행차 판단능력 등급화 방안 제시 및 시나리오별 등급화가 가능한 평가지표를 도출하고자 하였다. 드라이빙 시뮬레이터 실험에는 성인 30명(남=25, 여=5명)이 참여하였다. 실험결과 분석은 K-평균 군집분석과 독립표본 T-검정을 진행하였으며, 이를 통해 자율주행차의 판단능력 등급 분류가 가능함과 등급 분류의 통계적 유의성을 확인할 수 있었다. 향후 자율주행차의 위험상황 회피능력에 대한 신뢰수준을 향상시키는데 크게 기여할 수 있을 것이다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(2021R1I1A3055993). This paper has been written by modifying and supplementing the KSCE 2023 CONVENTION paper.

References

  1. Gregersen, N. P. and Nyberg, A. (2002). "Privat ovningskorning: en undersokning om hur den utnyttjas och om dess for-och nackdelar for trafiksakerheten." Swedish National Road and Transport Research Institute, VTI-report 481.
  2. Han, H. S., Lee, S. J., Shim, H. Y., Kim, S. H. and Yang, J. H. (2020). "A study on the driver's response time under jaywalking situation using simulator." Transaction of the Korean Society of Automotive Engineers, KSAE, Vol. 28, No. 7, pp. 471-481, https://doi.org/10.7467/KSAE.2020.28.7.471 (in Korean).
  3. Hobbs, C. A. and McDonough, P. J. (1998). "Development of the European new car assessment programme (Euro NCAP)." Regulation, NHTSA, Vol. 44, No. 3, pp. 2439-2453.
  4. ISO 21448:2022 (2022). Road vehicles-Safety of the intended functionality, Available at: https://www.iso.org/standard/77490.html (Accessed: January 12, 2023) (in Korean).
  5. ISO 26262:2018 (2018). Road vehicles-Safety Part 1 : Vocabulary, Available at: https://www.iso.org/standard/68383.html (Accessed: January 12, 2023).
  6. Japan Automobile Research Institute (JARI). (2019). FY 2018 Research, Development, and Demonstration Project for Social Implementation of Advanced Automated Driving Systems: Research and Development Project for Construction of Safety Evaluation Technology for Automated Driving Systems, General Foundation Japan Automobile Research Institute, Tokyo, Japan (in Japnese).
  7. Land, Infrastructure and Transport, Korean Law Information Center, 「Motor Vehicle Management Act」 Aricle 26 (Permission for Temporary Operation), Act NO. 13486, Aug. 11, 2015.
  8. Li, X., Rakotonirainy, A. and Yan, X. (2019). "How do drivers avoid collisions? A driving simulator-based study." Journal of Safety Research, Elsevier, Vol. 70, pp. 89-96, https://doi.org/10.1016/j.jsr.2019.05.002.
  9. Ministry of Land, Infrastructure and Transport(MOLIT) (2016). Road Design Standards (in Korean).
  10. Ministry of Land, Infrastructure and Transport(MOLIT) (2018). Development of High-Risk Driver Behavior Improvement and Violation Deterrence Technologies (in Korean).
  11. Ministry of Land, Infrastructure and Transport(MOLIT) (2020). Rules About the Road Structure & Facilities Standards (in Korean).
  12. SAE (2021). Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles J3016_ 202104, SAE, Available at: https://www.sae.org/standards/content/j3016_202104/ (Accessed: January 6, 2023).
  13. Shi, W., Alawieh, M. B., Li, X. and Yu, H. (2017). "Algorithm and hardware implementation for visual perception system in autonomous vehicle: A survey." Integration, Elsevier, Vol. 59, pp. 148-156, https://doi.org/10.1016/j.vlsi.2017.07.007.
  14. So, J. H. (2021). "Autonomous driving algorithms." Journal of TTA, Vol. 197, pp. 63-69 (in Korean).
  15. Spolander, K. (1983). "Bilforares upppfattning om egen korformaga [Drivers' assessment of their own driving ability]." Linkoping, Sweden: Vag-och transportforskningsinstitutet, VTI rapport Nr. 252.
  16. Traffic Accident Analysis System(TAAS) (2022). Traffic Accident Statistical Analysis, KoROAD, Available at: http://taas.koroad.or.kr/ (Accessed: January 24, 2023).
  17. Tronsmoen, T. (2008). "Associations between self-assessment of driving ability, driver training and crash involvement among young drivers." Transportation Research Part F: Traffic Psychology and Behaviour, Elsevier, Vol. 11, No. 5, pp. 334-346, https://doi.org/10.1016/j.trf.2008.02.002.