Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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A Study on Development of High Risk Test Scenario and Evaluation from Field Driving Conditions for Autonomous Vehicle

실도로 주행 조건 기반의 자율주행자동차 고위험도 평가 시나리오 개발 및 검증에 관한 연구

  • 정승환 (현대자동차 ADAS성능개발팀) ;
  • 유제명 (현대자동차 ADAS성능개발팀) ;
  • 정낙승 (현대자동차 ADAS성능개발팀) ;
  • 유민상 (현대자동차 법규인증1팀) ;
  • 편무송 (현대자동차 법규인증1팀) ;
  • 김재부 (현대자동차 법규인증1팀)
  • Received : 2018.10.29
  • Accepted : 2018.12.21
  • Published : 2018.12.31
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Abstract

Currently, a lot of researches about high risk test scenarios for autonomous vehicle and advanced driver assistance systems have been carried out to evaluate driving safety. This study proposes new type of test scenario that evaluate the driving safety for autonomous vehicle by reconstructing accident database of national automotive sampling system crashworthiness data system (NASS-CDS). NASS-CDS has a lot of detailed accident data in real fields, but there is no data of accurate velocity in accident moments. So in order to propose scenario generation method from accident database, we try to reconstruct accident moment from accident sketch diagram. At the same step, we propose an accident of occurrence frequency which is based on accident codes and road shapes. The reconstruction paths from accident database are integrated into evaluation of simulation environment. Our proposed methods and processor are applied to MILS (Model In the Loop Simulation) and VILS (Vehicle In the Loop Simulation) test environments. In this paper, a reasonable method of accident reconstruction typology for autonomous vehicle evaluation of feasibility is proposed.

Keywords

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Fig. 1 German in-Depth Accident Study Database

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Fig. 2 Driving Vehicle Environments of SHRP-2 Database

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Fig. 3 Classification and Reconstruction Process of Accident Database

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Fig. 4 Accident and Road Type Classification

Table 4 Results Analysis of Accident Database

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Fig. 6 Real Accident Diagram in NASS CDS Database

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Fig. 7 Reconstruction of Accident Driving Path

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Fig. 8 Accident Database Classification Type and Reconstruction Process

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Fig. 5 Classification and Reconstruction Process of Accident Database

Table 1 Comparison NASS CDC and SHRP-2

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Table 2 4-Phase Process of Accident Reconstruction

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Table 3 Accident Reconstruction Type and Equations

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Table 3 Accident Reconstruction Type and Equations (Continued)

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Table 4 Results Analysis of Accident Database (Continued)

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Table 5 Result of Accident Reconstruction

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Table 6 Accident Reconstruction – MILS Test Validation

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Table 7 Accident Reconstruction – VILS Test Validation

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References

  1. Adit Joshi, "Hardware-in-the-Loop (HIL) Implementation and Validation of SAE Level 2 Autonomous Vehicle with Subsystem Fault Tolerant Fallback Performance for Takeover Scenarios", SAE 2017-01-1994, 2017.
  2. Helene G. Moorman, Andrea Niles, and Caroline Crump, Audra Krake, Benjamin Lester, Laurene Milan, Christy Cloninger, David Cades, and Douglas Young, "Lane-Keeping Behavior and Cognitive Load with Use of Lane Departure Warning", SAE 2017-01-1407, 2017.
  3. Michael Holland, Jonathan Gibb, Kacper Bierzanowski, Stuart Rowell, Bo Gao, Chen Lv and Dongpu Cao, "Virtual Test Design and Automated Analysis of Lane Keeping Assistance Systems in Accordance with Euro NCAP Test Protocols", SAE 2017-01-0429, 2017.
  4. Sagar Kirtany, Vinay Kaul, Sushant Rai, Vijender Kumar Saini, Parmeet Singh, and Pankaj Shyoran, "An Integrated Test Suite for Verification and Validation of Advanced Driver Assistance Systems", SAE 2016-28-0216, 2016.
  5. S. Chung and H. Lee, "Vehicle Sideslip Estimation and Compensation for Banked Raod", International Journal of Automotive Technology, Vol. 17, No. 1, 63-69, 2016. https://doi.org/10.1007/s12239-016-0005-1
  6. Yufan Zeng, Xichan Zhu, "Critical Driving Scenarios Extraction Optimization Method Based on China-FOT Naturalistic Driving Study Database", SAE 2018-01-1628, 2018.
  7. Dietmar Otte, Martin Urban and Heiko Johannsen, "The New Approach of In-Depth-Accident-Investigation based on the Methodology for Traffic Accident Database on Scenarios TADS", SAE 2018-01-1628, 2018.
  8. Seunghwan Chung, Je Myoung Ryu, Nakseung Chung, Minsang Yu, Moo Song Pyun, Jae Bu Kim, "A Study on Reconstruction Typology of Scenario for Autonomous Vehicle Evaluation", KASA 2018 Fall Conference, 2018.
  9. Hamezh Alzu'bi, Brian Dwyer, Sarika Nagaraj, Martin Pischinger, Alanna Quail, "Cost Effect Automotive Platform for ADAS and Autonomos Development", SAE 2018-01-0588, 2018
  10. Kusano KD and Gabler HC, "Comparison and Validation of Injury Risk Classifiers for Advanced Automated Crash Notification Systems", Traffic Injury Prevention v15, pp. 126-133, 2014. https://doi.org/10.1080/15389588.2014.927577

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  1. Design for AEBS Test Scenario Applying Domestic Traffic Accidents vol.9, pp.4, 2018, https://doi.org/10.7236/ijasc.2020.9.4.1