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Study on the Improvement of Traffic Accident Report for Automated Vehicle Test Scenarios

자율주행 안전성 검증 시나리오 개발 활용을 위한 교통사고보고서 개선방향에 관한 연구

  • OH, Gyungtaek (Dept. of Transportation Eng., Univ. of Ajou) ;
  • KO, Woori (Dept. of Transportation Eng., Univ. of Ajou) ;
  • PARK, Jihyeok (Dept. of Transportation Eng., Univ. of Ajou) ;
  • YUN, Ilsoo (Dept. of Transportation System Eng., Univ. of Ajou) ;
  • SO, Jaehyun (Jason) (Dept. of Transportation System Eng., Univ. of Ajou)
  • 오경택 (아주대학교 교통공학과) ;
  • 고우리 (아주대학교 교통공학과) ;
  • 박지혁 (아주대학교 교통공학과) ;
  • 윤일수 (아주대학교 교통시스템공학과) ;
  • 소재현 (아주대학교 교통시스템공학과)
  • Received : 2022.02.08
  • Accepted : 2022.04.12
  • Published : 2022.04.30

Abstract

The accident data attributes of the traffic accident report are used not only in traditional traffic safety-related research to identify the cause of traffic accidents, but also as basis data for the development of the automated vehicle driving performance verification scenarios. However, since the data attributes of the traffic accident report are limited for the purpose of reconstructing the traffic situation and developing scenarios, this study aims to provide the directions for improvement of traffic accident report, ultimately for its expanded usability for the automated vehicle test scenarios. The directions for improvement of the traffic accident report are provided by categorizing the traffic situation before the accident (pre-crash), the situation immediately before or during the accident (on-crash), and the situation after the accident (post-crash), respectively. Additional data items or data processing methods are presented. Furthermore, data elements that can be extracted from the traffic accident process data in the unstructured narrative form are explored and provided.

교통사고보고서 상의 교통사고 관련 자료 속성들은 그 원인을 파악하고자 하는 전통적인 교통안전 관련 연구에서 뿐만 아니라 최근 자율주행자동차의 안전성 검증 시나리오 개발을 위한 연구에서도 활용될 수 있다. 다만, 교통사고보고서의 자료 속성들은 교통사고 상황 재현 및 시나리오 개발만을 위해 정의된 항목들이 아니므로, 본 연구에서는 확대된 활용성 측면의 교통사고보고서의 개선방향을 제시하고자 한다. 교통사고보고서의 개선방향은 각각 교통사고 발생 이전 상황(pre-crash), 교통사고 중 상황(on-crash), 교통사고 발생 이후 상황(post-crash)로 구분하여 제시하였으며, 각 구분에 따른 추가 자료 항목 또는 자료 처리 방안에 대하여 제시하였다. 또한, 정형화된 형태의 교통사고자료 외에 비정형화된 서술 형태의 교통사고 경위자료로부터 추출 가능한 정보항목들을 도출하여 제시하였다.

Keywords

Acknowledgement

본 과제(결과물)은 교육부와 한국연구재단의 재원으로 지원을 받아 수행된 디지털 신기술 인재양성 혁신공유대학사업의 연구 결과입니다.

References

  1. An, H. and Lee, S.(2015), "The analysis of data structure to digital forensic of dashboard camera", Journal of the Korea Institute of Information Security & Cryptology, vol. 25, no. 6, pp.1495-1502. https://doi.org/10.13089/JKIISC.2015.25.6.1495
  2. Char, F. and Serre, T.(2020), "Analysis of pre-crash characteristics of passenger car to cyclist accidents for the development of advanced drivers assistance systems", Accident Analysis & Prevention, vol. 136, 105408. https://doi.org/10.1016/j.aap.2019.105408
  3. Chen, T., Shi, X. and Wong, Y. D.(2019), "Key feature selection and risk prediction for lane-changing behaviors based on vehicles' trajectory data", Accident Analysis & Prevention, vol. 129, pp.156-169. https://doi.org/10.1016/j.aap.2019.05.017
  4. Cryer, P. C., Westrup, S., Cook, A. C., Ashwell, V., Bridger, P. and Clarke, C.(2001), "Investigation of bias after data linkage of hospital admissions data to police road traffic crash reports", Injury Prevention, vol. 7, no. 3, pp.234-241. https://doi.org/10.1136/ip.7.3.234
  5. Econovill, http://www.econovill.com/news/articleView.html?idxno=352294, 2021.11.18.
  6. Eo, H., Xu, G. and Lee, S.(2019), "Effective Multimodal Design for Safe Hand Over in Autonomous Vehicles: With a Focus on Avoiding Accident Scenarios", Journal of the Human Computer Interaction Society of Korea, vol. 13, no. 3, pp.21-28.
  7. Ha, M. and Ahn, H.(2019), "A Machine Learning-Based Vocational Training Dropout prediction Model Considering Structured and Unstructured Data", The Journal of the Korea Contents Association, vol. 19, no. 1, pp.1-15. https://doi.org/10.5392/JKCA.2019.19.01.001
  8. Ha, O. K., Oh, J. T., Won, J. M. and Sung, N. M.(2005), "The study on the accident injury severity using ordered probit model", Journal of Korean Society of Transportation, vol. 23, no. 4, pp.47-55.
  9. Ha, W. S. and Han, S. Y.(2003), "Establishment of Important Impact Parameters of Traffic Accident Reconstruction Program'PC-CRASH'", Journal of Korean Society of Transportation, vol. 21, no. 2, pp.155-164.
  10. Han, C. P.(2021), "Analysis of vehicle progress before and after a collision using simulation", Journal of the Korea Academia-Industrial Cooperation Society, vol. 22, no. 1, pp.402-408.
  11. Hur, Y., Lee, C., Kim, G. and Lim, H.(2019), "Topic Automatic Extraction Model based on Unstructured Security Intelligence Report", Journal of the Korea Convergence Society, vol. 10, no. 6, pp.33-39. https://doi.org/10.15207/JKCS.2019.10.6.033
  12. Hwang, G. S., Choe, J. S., Kim, S. Y., Heo, T. Y., Jo, W. B. and Kim, Y. S.(2010), "Freeway Crash Frequency Model Development Based on the Road Section Segmentation by Using Vehicle Speeds", Journal of Korean Society of Transportation, vol. 28, no. 2, pp.151-159.
  13. Jeong, H., Kim, H., Park, S., Han, E., Kim, K. H. and Yun, I.(2017), "Prediction of Severities of Rental Car Traffic Accidents using Naive Bayes Big Data Classifier", The Journal of The Korea Institute of Intelligent Transport Systems, vol. 16, no. 4, pp.1-12.
  14. Jung, K. Y. and Bae, S. H.(2017), "Forecasting of Probability of Accident by Analizing the Traffic Accident Data: Main Intersections on Arterial Roads in Busan", Korean Society of Civil Engineers Journal of Civil and Environmental Engineering Research, vol. 37, no. 1, pp.111-117. https://doi.org/10.12652/Ksce.2017.37.1.0111
  15. Jung, W. J.(2019), "A Study on the Cause Analysis and Countermeasures of Traffic Accidents-Focused on Chungnam and Sejong Regions", Korea Community Welfare Policy Association, vol. 30, no. 1, pp.143-164.
  16. Kang, J. G. and Lee, S.(2002), "Traffic accident prediction model by freeway geometric types", Journal of Korean Society of Transportation, vol. 20, no. 4, pp.163-175.
  17. Kim, G., Lim, J., Park, I., Chun, Y. and Cho, C.(2013), "A Study on Traffic Accident Reconstruction through Vehicle Crash Test", Transactions of the Korean Society of Automotive Engineers, vol. 21, no. 6, pp.58-63. https://doi.org/10.7467/KSAE.2013.21.6.058
  18. Kim, J. H. and Kim, S. S.(2021), "A Study on the Analysis of Agricultural R&D Keywords Using Textmining Method", Journal of the Korea Academia-Industrial Cooperation Society, vol. 22, no. 2, pp.721-732. https://doi.org/10.5762/KAIS.2021.22.11.721
  19. Kim, J. U., Nam, G. M., Kim, J. H. and Lee, S. B.(2006), "Development of traffic accidents prediction model with fuzzy and neural network theory", Journal of Korean Society of Transportation, vol. 24, no. 7, pp.81-90.
  20. Kim, S. H., Jang, J. A. and Choe, G. J.(2005), "A Hierarchical Approach for Diagnose of Safety Performance and Factor Identification for Black Spots (Black on Suwon-city)", Journal of Korean Society of Transportation, vol. 23, no. 1, pp.9-20.
  21. Kim, Y. Y., Cho, K. H. and Kim, Y.(2020), "Analysis of risk factors for traffic accidents in Daegu area", The Korean Data & Information Science Society, vol. 31, no. 3, pp.503-510. https://doi.org/10.7465/jkdi.2020.31.3.503
  22. Korea Transport Institute(2017), Estimation of Transport Accident Costs in 2015, pp.62-67.
  23. Lee, H. R., Kum, K. J. and Son, S. N.(2011), "A study on the factor analysis by grade for highway traffic accident", International Journal of Highway Engineering, vol. 13, no. 3, pp.157-165. https://doi.org/10.7855/IJHE.2011.13.3.157
  24. Lee, J. Y., Chung, J. H. and Son, B. S.(2008), "Analysis of traffic accident severity for Korean highway using structural equations model", Journal of Korean Society of Transportation, vol. 26, no. 2, pp.17-24.
  25. Lee, S. B., Han, D. H. and Lee, Y. I.(2015), "Development of freeway traffic incident clearance time prediction model by accident level", Journal of Korean Society of Transportation, vol. 33, no. 5, pp.497-507. https://doi.org/10.7470/jkst.2015.33.5.497
  26. Lee, S. C. and Oh, J. S.(2006), "The understanding of traffic accidents' patterns: The analysis of driver's behavior and location at the moment of traffic accident", The Journal of the Korea Data Analysis Society, vol. 8, no. 6, pp.2457-2471.
  27. Lee, S. H., Jeung, W. D. and Woo, Y. H.(2012), "Comparative Analysis of Elderly's and Non-Elderly's Human Traffic Accident Severity", The Journal of the Korea Institute of Intelligent Transport Systems, vol. 11, no. 6, pp.133-144. https://doi.org/10.12815/kits.2012.11.6.133
  28. Nitsche, P., Thomas, P., Stuetz, R. and Welsh, R.(2017), "Pre-crash scenarios at road junctions: A clustering method for car crash data", Accident Analysis & Prevention, vol. 107, pp.137-151. https://doi.org/10.1016/j.aap.2017.07.011
  29. Park, S. M., Han, E., Hong, Y. S., So, J. H. and Yun, I. S.(2017), "Development of Safety Evaluation Scenarios for Autonomous Vehicles in Community Roads Using a Text Mining Technique", Proceedings of Korea Institute of Intelligent Transport Systems Fall Conference 2017, pp.189-192.
  30. Park, S., Jeong, H., Kim, K. H. and Yun, I.(2018), "Development of safety evaluation scenario for autonomous vehicle take-over at expressways", The Journal of The Korea Institute of Intelligent Transport Systems, vol. 17, no. 2, pp.142-151. https://doi.org/10.12815/kits.2018.17.2.142
  31. Park, S., Park, S., Jeong, H., Yun, I. and So, J. J.(2021), "Scenario-mining for level 4 automated vehicle safety assessment from real accident situations in urban areas using a natural language process", Sensors, vol. 21, no. 20, p.6929. https://doi.org/10.3390/s21206929
  32. Park, S., So, J. J., Ko, H., Jeong, H. and Yun, I.(2019), "Development of Safety Evaluation Scenarios for Autonomous Vehicle Tests Using 5-Layer Format (Case of the Community Road)", The Journal of the Korea Institute of Intelligent Transport Systems, vol. 18, no. 2, pp.114-128. https://doi.org/10.12815/kits.2019.18.2.114
  33. Rajendran, G., Arthanari, M. and Sivakumar, M.(2011), "GPS Tracking Simulation by Path Replaying", International Journal of Innovative Technology & Creative Engineering, vol. 1, no. 1, pp.20-26.
  34. Scheurwegs, E., Luyckx, K., Luyten, L., Daelemans, W. and Van den Bulcke, T.(2016), "Data integration of structured and unstructured sources for assigning clinical codes to patient stays", Journal of the American Medical Informatics Association, vol. 23, no. e1, pp.e11-e19. https://doi.org/10.1093/jamia/ocv115
  35. Shanthi, S. and Ramani, R. G.(2011), "Classification of vehicle collision patterns in road accidents using data mining algorithms", International Journal of Computer Applications, vol. 35, no. 12, pp.30-37.
  36. Shi, X., Wong, Y. D., Li, M. Z. F. and Chai, C.(2018), "Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory", Accident Analysis & Prevention, vol. 117, pp.346-356. https://doi.org/10.1016/j.aap.2018.05.007
  37. Shim, J., Yu, J., Park, J. and Park, B.(2018), Estimation of transport accident costs in 2016, Sejong: The Korea Transport Institute, pp.29-54.
  38. Sohn, S. Y. and Shin, H. W.(2001), "Data mining for road traffic accident type classification", Ergonomics, vol. 44, pp.107-117. https://doi.org/10.1080/00140130120928
  39. Song, J. M.(2021), A Study on Efficient Use of Traffic Accident Statistics Tables and Improvement Measures, Master's Thesis, Hanbat National University, pp.56-57.
  40. Tian, R., Yang, Z. and Zhang, M.(2010), "Method of road traffic accidents causes analysis based on data mining", In 2010 International Conference on Computational Intelligence and Software Engineering, pp.1-4.
  41. Yoon, J. and Lee, S.(2018), "Analysis of Pedestrian Traffic Accident Factors around the Exclusive Median Bus Lane Station Area: Focused on TAAS (2014-2016) Data in Seoul", Journal of Korea Planning Association, vol. 53, pp.123-142. https://doi.org/10.17208/jkpa.2018.04.53.2.123
  42. Zhang, D., Yin, C., Zeng, J., Yuan, X. and Zhang, P.(2020), "Combining structured and unstructured data for predictive models: A deep learning approach", BMC Medical Informatics and Decision Making, vol. 20, no. 1, pp.1-11. https://doi.org/10.1186/s12911-019-1002-x