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The Extraction of Automated Vehicles Traffic Accident Factors and Scenarios Using Real-World Data

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Congress on Intelligent Systems

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 114))

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Abstract

As automated vehicles (AVs) approach commercialization, the fact that the SAFETY problem becomes more concentrated is not controversial. Depending on this issue, the scenarios research that can ensure safety and are related to vehicle safety assessments are essential. In this paper, based on ‘report of traffic collision involving an AVs’ provided by California DMV (Department of Motor Vehicles), we extract the major factors for identifying AVs traffic accidents to derive basic AVs traffic accident scenarios by employing the random forest, one of the machine learning. As a result, we have found the importance of the pre-collision movement of neighboring vehicles to AVs and inferred that they are related to collision time (TTC). Based on these factors, we derived scenarios and confirm that AVs rear-end collisions of neighboring vehicles usually occur when AVs are ahead in passing, changing lanes, and merge situations. While most accident determinants and scenarios are expected to be similar to those between human driving vehicles (HVs), AVs are expected to reduce accident rates because ‘AVs do not cause accidents.’

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References

  1. https://time.com/3719270/you-asked-how-do-driverless-cars-work/. Access 03.04.2021

  2. Navigant Leaderboard (2020) https://www.greencarcongress.com/2020/03/20200324-navigant.html

  3. Abraham H, Lee C, Brady S, Fitzgerald C, Mehler B, Reimer B, Coughlin JF (2017) Autonomous vehicles and alternatives to driving: trust, preferences, and effects of age. In: Proceedings of the transportation research board 96th annual meeting

    Google Scholar 

  4. Zhang T, Tao D, Qu X, Zhang X, Lin R, Zhang W (2019) The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transp Res Part C Emerg Technol 98:207–220

    Article  Google Scholar 

  5. Hartwich F, Witzlack C, Beggiato M, Krems JF (2019) The first impression counts—a combined driving simulator and test track study on the development of trust and acceptance of highly automated driving. Transp Res Part F Traff Psychol Behav 65:522–535

    Article  Google Scholar 

  6. Goodall NJ (2014) Ethical decision making during automated vehicle crashes. Transp Res Rec 2424(1):58–65

    Article  Google Scholar 

  7. Riedmaier S, Ponn T, Ludwig D, Schick B, Diermeyer F (2020) Survey on scenario-based safety assessment of automated vehicles. IEEE Access 8:87456–87477

    Article  Google Scholar 

  8. Kim K, Kim B, Lee K, Ko B, Yi K (2017) Design of integrated risk management-based dynamic driving control of automated vehicles. IEEE Intell Transp Syst Mag 9(1):57–73

    Article  Google Scholar 

  9. Kang M, Song J, Hwang K (2020) For preventative automated driving system (PADS): traffic accident context analysis based on deep neural networks. Electronics 9(11):1829

    Google Scholar 

  10. Lee H, Kang M, Song J, Hwang K (2020) The detection of black ice accidents for preventative automated vehicles using convolutional neural networks. Electronics 9(12):2178

    Google Scholar 

  11. Wen M, Park J, Cho K (2020) A scenario generation pipeline for autonomous vehicle simulators. HCIS 10:1–15

    Google Scholar 

  12. Ulbrich S, Menzel T, Reschka A, Schuldt F, Maurer M (2015) Defining and substantiating the terms scene, situation, and scenario for automated driving. In: 2015 IEEE 18th international conference on intelligent transportation systems. IEEE, pp 982–988

    Google Scholar 

  13. Webb N, Smith D, Ludwick C, Victor T, Hommes Q, Favaro F, Ivanov G, Daniel T (2020) Waymo. https://time.com/3719270/you-asked-how-do-driverless-cars-work/. Access: 03.04.2021’s safety methodologies and safety readiness determinations. arXiv:2011.00054

  14. Stellet JE, Zofka MR, Schumacher J, Schamm T, Niewels F, Zöllner JM (2015) Testing of advanced driver assistance towards automated driving: a survey and taxonomy on existing approaches and open questions. In: 2015 IEEE 18th international conference on intelligent transportation systems. IEEE, pp 1455–1462

    Google Scholar 

  15. De Gelder E, Paardekooper JP, Saberi AK, Elrofai H, Ploeg J, Friedmann L, De Schutter B (2020) Ontology for scenarios for the assessment of automated vehicles. arXiv:2001.11507

  16. Schwall M, Daniel T, Victor T, Favaro F, Hohnhold H (2020) Waymo public road safety performance data. arXiv:2011.00038

  17. Elrofai H, Paardekooper JP, de Gelder E, Kalisvaart S, den Camp OO (2018) Scenario-based safety validation of connected and automated driving. Netherlands Organization for Applied Scientific Research, TNO, Technical Report

    Google Scholar 

  18. Pütz A, Zlocki A, Bock J, Eckstein L (2017). System validation of highly automated vehicles with a database of relevant traffic scenarios. Situations 1:E5

    Google Scholar 

  19. Erdogan A, Ugranli B, Adali E, Sentas A, Mungan E, Kaplan E, Leitner A (2019) Real-world maneuver extraction for autonomous vehicle validation: a comparative study. In: 2019 IEEE intelligent vehicles symposium (IV). IEEE, pp 267–272

    Google Scholar 

  20. Webb N, Smith D, Ludwick C, Victor T, Hommes Q, Favaro F, Ivanov G, Daniel T (2020) Waymo’s Safety Methodologies and Safety Readiness Determinations. arXiv:2011.00054

  21. https://www.dmv.ca.gov/portal/vehicle-industry-services/autonomous-vehicles/autonomous-vehicle-collision-reports/

  22. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  23. Nadimi N, Ragland DR, Mohammadian Amiri A (2020) An evaluation of time-to-collision as a surrogate safety measure and a proposal of a new method for its application in safety analysis. Transp Lett 12(7):491–500

    Article  Google Scholar 

  24. Li Y, Wu D, Lee J, Yang M, Shi Y (2020) Analysis of the transition condition of rear-end collisions using time-to-collision index and vehicle trajectory data. Accident Anal Prevent 144:105676

    Google Scholar 

  25. https://time.com/5205767/uber-autonomous-car-crash-arizona/. Access 10.03.2021

  26. Lund UJ (2005) The effect of seating location on the injury of properly restrained children in child safety seats. Accid Anal Prev 37(3):435–439

    Article  Google Scholar 

  27. Choi J (2017) Multinomial logit framework to evaluate the impact of seating position on senior occupant injury severity in traffic accidents. J Korean Soc Safe 32(3):141–150

    Google Scholar 

  28. Viano DC, Parenteau CS, Edwards ML (2007) Rollover injury: effects of near-and far-seating position, belt use, and number of quarter rolls. Traffic Inj Prev 8(4):382–392

    Article  Google Scholar 

  29. Koppel S, Jiménez Octavio J, Bohman K, Logan D, Raphael W, Quintana Jimenez L, Lopez-Valdes F (2019) Seating configuration and position preferences in fully automated vehicles. Traffic Inj Prev 20(sup2):S103–S109

    Article  Google Scholar 

  30. Lopez-Valdes FJ, Bohman K, Jimenez-Octavio J, Logan D, Raphael W, Quintana L, Fueyo RSD, Koppel S (2020) Understanding users’ characteristics in the selection of vehicle seating configurations and positions in fully automated vehicles. Traffic injury prevention, pp 1–6

    Google Scholar 

  31. Forman J, Lin H, Gepner B, Wu T, Panzer M (2018) Occupant safety in automated vehicles—effect of seatback recline on occupant restraint. JSAE, Paper Number 20185234

    Google Scholar 

  32. Jin X, Hou H, Shen M, We H, Yang K (2018) Occupant kinematics and biomechanics with rotatable seat in autonomous vehicle collision: a preliminary concept and strategy. IRCOBI, Athens, Greece. Sept 12–14

    Google Scholar 

  33. Kitagawa Y, Hayashi S, Yamada K, Gotoh M (2017) Occupant kinematics in simulated autonomous driving vehicle collisions: influence of seating position, direction and angle. Stapp Car Crash J 61:101–155

    Google Scholar 

  34. US Department of Transportation (2018) Preparing for the future of transportation: automated vehicles 3.0

    Google Scholar 

  35. Harb R, Yan X, Radwan E, Su X (2009) Exploring precrash maneuvers using classification trees and random forests. Accid Anal Prev 41(1):98–107

    Article  Google Scholar 

  36. De Oña J, Mujalli RO, Calvo FJ (2011) Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accid Anal Prev 43(1):402–411

    Article  Google Scholar 

  37. Rolison JJ, Regev S, Moutari S, Feeney A (2018) What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records. Accid Anal Prev 115:11–24

    Article  Google Scholar 

  38. Zeng Q, Gu W, Zhang X, Wen H, Lee J, Hao W (2019) Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors. Accid Anal Prev 127:87–95

    Article  Google Scholar 

  39. Tang J, Liang J, Han C, Li Z, Huang H (2019) Crash injury severity analysis using a two-layer Stacking framework. Accid Anal Prev 122:226–238

    Article  Google Scholar 

  40. Kim K, Cho SA (2020) Lessens learned from crash types of automated vehicles: based on accident data of automated vehicles in California, USA. Korean Soc Transp 17(2):34–42 (9 p). (in Trans)

    Google Scholar 

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Acknowledgements

This work is supported by Ministry of Land, Infrastructure and Transport of Korea (grant 21AMDP-C161754-01) and 2020 Hongik University Research Fund.

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Authors and Affiliations

  1. Hongik University, Seoul, 04066, Republic of Korea

    MinHee Kang, Jaein Song & Keeyeon Hwang

Authors
  1. MinHee Kang
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  2. Jaein Song
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  3. Keeyeon Hwang
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Corresponding author

Correspondence to MinHee Kang .

Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India

    Harish Sharma

  3. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

    K. Balachandran

  4. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Kang, M., Song, J., Hwang, K. (2022). The Extraction of Automated Vehicles Traffic Accident Factors and Scenarios Using Real-World Data. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-16-9416-5_1

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