Abstract
This study proposes a Big Data technique-based crash scene re-construction method in order to provide a systematic and effective way of developing critical traffic safety test scenarios for automated vehicles. It is widely understood that crashes occur through conflicts between road users and unsafe traffic conditions. Crash data are thus a useful resource to capture unsafe traffic conditions. Nevertheless, in practice many of the automated vehicle traffic test scenarios have been developed based on engineer’s judgment or aggregated crash statistics and studies, because crash data are too abundant to be comprehensively investigated. The proposed Big Data technique-based method provides the following innovations in developing automated vehicle test scenarios: abundant crash data can be investigated at once and in a short period of time using a text-weight analysis, which is one of the representative Big Data analytics; a comprehensive set of automated vehicle test scenarios can be developed by taking all significant words that frequently appear in crash descriptions into consideration; and the method can even be flexible in terms of capturing significant words by excluding or including specific words and setting a threshold of the word frequency. This proposed Big Data technique-based method is validated in comparison with the resulting test scenarios from a manual investigation of crash data, and it was found that 14 of a total of 18 scenarios correspond to the scenarios from manual investigation and the other four scenarios are additionally derived by the proposed approach. With these innovations in mind, the Big Data technique-based method proposed in this study is not only a systematic and practical approach but also leads to the development of a comprehensive set of automated vehicle test scenarios, by comprehensively taking into consideration significant words of crash descriptions, which should be carefully considered in developing automated vehicle test scenarios.
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References
BMWE (2014). PEGASUS Research Project, Braunschweig, Germany, https://www.pegasusprojekt.de/en/about-PEGASUS [Accessed on January 5, 2018].
Chrysler, S. T., Ahmad, O., and Schwarz, C. W. (2015). “Creating pedestrian crash scenarios in a driving simulator environment.” Traffic Injury Prevention, Vol. 16, No. supl, pp. S12–S17.
Dianov, I., Ramirez-Amaro, K., and Cheng, G. (2015). “Generating compact models for traffic scenarios to estimate driver behavior using semantic reasoning.” Proc., International Conference on Intelligent Robots and Systems, IROS, Hamburg, Germany.
Fagerlind, H., Heinig, I., Viström, M., Wisch, M., Sulzberger, L., McCarthy, M., and Schaub, S. (2010). “Analysis of accident data for test scenario definition in the ASSESS project.” Proc. 4th International Conference ESAR-Expert Symposium on Accident Research, Hannover, Germany.
Ference, J. J., Szabo, S., and Najm, W. G (2007). “Objective test scenarios for integrated vehicle-based safety systems.” Proc. 20th International Technical Conference on Enhanced Safety of Vehicles (ESV), Lyon, France.
Gao, L. and Wu, H. (2013). Verb-based text mining of road crash report. No. 13-2292, Transportation Research Board, Washington, D.C., USA.
General Motors (1997). 44-Crashes, NAO Engineering, Safety & Restraints Center, Crash Acoidance Department, General Motors Corporation, Detroit, MI, USA.
Hong, D., Jeong, H., Park, S., Han, E., Kim, H., and Yun, I. (2017). “Study on the methodology for extracting information from SNS using a sentiment analysis.” The Journal of The Korea Institute of Intelligent Transport Systems, Vol. 16, No. 6, pp. 141–155.
HORIBA-MIRA (2017). MIRA-City circuit facility, Warwickshire, UK, https://www.horiba-mira.com/our-services/city-circuit [Accessed on March 7, 2018].
Im, I., Song, J., Lee, J., and Hwang, K. (2017). “Analysis of the perception of autonomous vehicles using text mining technique.” The Journal of The Korea Institute of Intelligent Transport Systems, Vol. 16, No. 6, pp. 231–243.
ITU (2014). Smart sustainable cities: An analysis of definitions, Report FG-SSC, ITU-T, Geneva, Switzerland, https://www.itu.int/en/ITU-T/focusgroups/ssc/Documents/Approved_Deliverables/TR-Definitions.docx [Accessed on January 5, 2018]
JARI (2017). J-Town—automated vehicles test-bed, JARI, Osaka, Japan, http://www.jari.or.jp/tabid/142/Default.aspx [Accessed on March 7, 2018].
MOLEG (2014). Regulations for highways and related facilities, Ministry of Government Legislation, Sejong, Korea.
So, J. J., Moon, Y. J., and Hong, A. Y. (2017). “K-City: A Korea’s test-bed for automated vehicles.” Proc., 2017 Automated Vehicle Symposium, TRB, Washington, D.C., USA.
UMICH (2017). M-City-Automated vehicles test-bed, University of Michigan, Ann Arbor, MI, USA, https://mcity.umich.edu/ [Accessed on March 7, 2018].
WAYMO (2017). Waymo safety report: On the road to fully self-driving, Mountain View, CA, USA, from https://storage.googleapis.com/sdc-prod/v1/safety-report/waymo-safety-report-2017.pdf [Accessed on August 12, 2018].
Acknowledgements
This work was supported by Institute for Giga KOREA Foundation (GKF)/Information & communications Technology Promotion (IITP) grant funded by the Korea Government (MSIT). (No.GK18N0500, 5G V2X Convergence Technology Development and Trial for Autonomous Driving and C-ITS Service).
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So, J.J., Park, I., Wee, J. et al. Generating Traffic Safety Test Scenarios for Automated Vehicles using a Big Data Technique. KSCE J Civ Eng 23, 2702–2712 (2019). https://doi.org/10.1007/s12205-019-1287-4
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DOI: https://doi.org/10.1007/s12205-019-1287-4