Skip to main content
SpringerLink
Log in

Surrogate Measures of Automated Vehicle Safety

  • Conference paper
  • First Online:
Road Vehicle Automation 10 (ARTSymposium 2022)

Part of the book series: Lecture Notes in Mobility ((LNMOB))

Included in the following conference series:

Abstract

Surrogate measures of traffic safety replace collision statistics as a means of assessing the safety of a roadway, intersection, vehicle, or mobility system. Effective and consistent surrogate measures of traffic risk and safety that will be useful to ADS stakeholders — including AV developers, traffic infrastructure developers and managers, regulators, legislators, and the public — will have a number of essential characteristics, including monotonicity and scalability.

Trailing indicators, such as collision statistics, are a poor methodology for improving safety, In addition, the use of trailing indicators incurs pain and loss on society, and is not an ethically acceptable approach. Leading indicators, based on non-collision interactions, include: Traffic Conflicts, Time-to-Collision (TTC), Post-Encroachment Time (PET), Instantaneous Safety Metric (ISM), harsh accelerations and turns (generally measured by an inertial measurement unit (IMU)), AV Control System Disengagements, and near-misses or near-crash events.

Surrogate measures, reviewed here, gather, process, and in some cases predict traffic movement, or control system behavior, and produce a (sometimes quantitative) score reflecting the riskiness or safeness of the behavior of vehicles in traffic.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.mobileye.com/responsibility-sensitive-safety/.

  2. 2.

    13 CCR §227.50 Reporting Disengagement of Autonomous Mode:

    For the purposes of this section, “disengagement” means a deactivation of the autonomous mode when a failure of the autonomous technology is detected or when the safe operation of the vehicle requires that the autonomous vehicle test driver disengage the autonomous mode and take immediate manual control of the vehicle, or in the case of driverless vehicles, when the safety of the vehicle, the occupants of the vehicle, or the public requires that the autonomous technology be deactivated. (b) Every manufacturer authorized under this article to test autonomous vehicles on public roads shall prepare and submit to the department an annual report summarizing the information compiled pursuant to subsection (a) by January 1st, of each year. [41]

    .

Abbreviations

ADS:

Automated Driving System [1]

AV:

Automated Vehicle; Autonomous Vehicle

IMU:

Inertial Measurement Unit

ISM:

Instantaneous Safety Metric [2]

MPrISM:

Model Predictive Instantaneous Safety Metric [3]

PET:

Post-Encroachment Time [4]

RSS:

Responsibility-Sensitive Safety [5]

TCT:

Traffic Conflicts Technique [6]

TTC:

Time-to-Collision [7]

NHTSA:

National Highway Traffic Safety Administration, U.S. Department of Transportation

References

  1. Browne, D.: Automated Driving Systems (ADS) - An introduction to technology and vehicle connectivity - Part 3, Association for the Advancement of Automotive Medicine (AAAM). https://www.aaam.org/automated-driving-systems-ads-introduction-technology-vehicle-connectivity/. Accessed 4 Feb 2023

  2. Every, J.L., Barickman, F., Martin, J., Rao, S., Schnelle, S., Weng, B.: A novel method to evaluate the safety of highly automated vehicles. In: 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV) National Highway Traffic Safety Administration, Detroit, Michigan (2017). https://www-esv.nhtsa.dot.gov/Proceedings/25/25ESV-000076.pdf

  3. Weng, B., Rao, S.J., Deosthale, E., Schnelle, S., Barickman, F.: Model predictive instantaneous safety metric for evaluation of automated driving systems. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 1899–1906. IEEE (2020). https://ieeexplore.ieee.org/abstract/document/9304635

  4. Allen, B.L., Tom, S.B.: Analysis of traffic conflicts and collisions. Transp. Res. Rec. 667, pp. 67–74 (1978). Paper sponsored by Committee on Methodology for Evaluating Highway Improvements, Transportation Research Board, U.S. National Academies of Science, Engineering, Medicine. https://onlinepubs.trb.org/Onlinepubs/trr/1978/667/667-009.pdf

  5. Shalev-Shwartz, S., Shammah, S., Shashua, A.: On a formal model of safe and scalable self-driving cars. arXiv preprint arXiv:1708.06374 (2017). https://arxiv.org/pdf/1708.06374.pdf

  6. Perkins, S.R., Harris, J.I.: Traffic conflict characteristics: accident potential at intersections. Highway Res. Rec. 225, 35–43 (1968). Paper sponsored by Committee on Traffic Safety and presented at the 47th Annual Meeting. https://onlinepubs.trb.org/Onlinepubs/hrr/1968/225/225-004.pdf

  7. Hayward, J.C.: Near-miss determination through use of a scale of danger. In: Proceedings of the 51st Annual Meeting of the Highway Research Board. Washington, DC: Transportation Research Board, U.S. National Academies of Science, Engineering, Medicine, January 1972, pp. 24–34. https://onlinepubs.trb.org/Onlinepubs/hrr/1972/384/384-004.pdf

  8. Perkins, S.R., Harris, J.I.: Traffic conflict characteristics: accident potential at intersections. General Motors Research Publication, Lansing, MI, Technical report GMR-718, December 1967

    Google Scholar 

  9. Güttinger, V.A.: From accidents to conflicts: alternative safety measurement. In: Proceedings of the 3rd International Workshop on Traffic Conflicts Techniques, Kraay, J.H. (ed.) no. R-82-27. Leidschendam, The Netherlands: The International Committee on Traffic Conflicts Techniques ICTCT, April 1982, pp. 14–25 (1982). https://www.ictct.net/wp-content/uploads/XX-Leidschendam-1982/1982-Proceedings.pd#page=14

  10. Gettman, D., Head, L.: Surrogate safety measures from traffic simulation models, final report," U.S. Department of Transportation, Federal Highway Administration, Office of Safety Research and Development, Technical report, FHWA-RD-03-050, January 2003. https://www.fhwa.dot.gov/publications/research/safety/03050/

  11. Gettman, D., Pu, L., Sayed, T., Head, L.: Surrogate safety assessment model and validation: Final report, U.S. Department of Transportation, Federal Highway Administration, Office of Safety Research and Development, Tech. Rep. FHWA-HRT-08-051, June 2008. https://www.fhwa.dot.gov/publications/research/safety/08051/

  12. Tarko, A., Davis, A.G., Saunier, N., Sayed, T., Washington, S.: Surrogate measures of safety, ANB20(3) Subcommittee on Surrogate Measures of Safety, ANB20 Committee on Safety Data Evaluation and Analysis, Transportation Research Board, U.S. National Academies of Science, Engineering, Medicine, pp. 1–13, January 2009, white Paper. https://www.researchgate.net/publication/245584894_Surrogate_Measures_of_Safety

  13. Guo, F., Klauer, S.G., McGill, M.T., Dingus, T.A.: Evaluating the relationship between near-crashes and crashes: can near-crashes serve as a surrogate safety metric for crashes? United States. Department of Transportation. National Highway Traffic Safety Administration, Technical report DOT-HS-811-382, October 2010. https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/dot_hs_811_382.pdf

  14. Ariza, A.: Validation of road safety surrogate measures as a predictor of crash frequency rates on a large-scale microsimulation, Master’s thesis, University of Toronto, Department of Civil Engineering, Toronto, Canada, June 2011. https://tspace.library.utoronto.ca/bitstream/1807/30160/1/Ariza_Alexander_201111_MASc_thesis.pdf

  15. Yang, H.: Simulation-based evaluation of traffic safety performance using surrogate safety measures. Ph.D. dissertation, Rutgers University, Department of Civil and Environmental Engineering, New Brunswick, NJ, January 2012. https://rucore.libraries.rutgers.edu/rutgers-lib/36680/

  16. Lorion, A.C., Persaud, B.: Investigation of surrogate measures for safety assessment of urban two-way stop controlled intersections. Can. J. Civ. Eng. 52(12), 987–992 (2015). https://cdnsciencepub.com/doi/pdf/10.1139/cjce-2015-0023?download=true

  17. Ušpalytė-Vitkūnienė, R., Laureshyn, A.: Perspectives for surrogate safety studies in East-European countries. Baltic J. Road Bridge Eng. 3, 161–166 (2017). https://bjrbe-journals.rtu.lv/article/download/bjrbe.2017.19/1762

  18. Johnsson, C., Laureshyn, A., De Ceunynck, T.: In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators. Transp. Rev. (2018). https://ictct.net/wp-content/uploads/SMoS_Library/LIB_Johnsson_2018.pdf

  19. Tarko, A.: Measuring Road Safety with Surrogate Events. Elsevier Inc, Amsterdam (2019). https://www.elsevier.com/books/measuring-road-safety-using-surrogate-events/9780128105047

  20. Johnsson, C.: Surrogate measures of safety with a focus on vulnerable road users: an exploration of theory, practice, exposure, and validity, Ph.D. dissertation, Lund University, Faculty of Engineering, Department of Technology and Society, Transport and Roads, Lund, Sweden, September 2020. https://portal.research.lu.se/en/publications/surrogate-measures-of-safety-with-a-focus-on-vulnerable-road-user

  21. Saunier, N., Laureshyn, A.: Surrogate measures of safety. In: Vickerman, R. (ed.) International Encyclopedia of Transportation, pp. 662–667. Elsevier, Oxford (2021). https://www.sciencedirect.com/science/article/pii/B9780081026717101976

  22. Ambros, J., Jurewicz, C., Chevalier, A., Valentová, V.: Speed-related surrogate measures of road safety based on floating car data. In: Macioszek, E., Sierpiński, G. (eds.) Research Methods in Modern Urban Transportation Systems and Networks. LNNS, vol. 207, pp. 129–144. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-71708-7_9

    Chapter  Google Scholar 

  23. Lu, J., Grembek, O., Hansen, M.: Connecting surrogate safety measures to crash probablity via causal probabilistic time series prediction, October 2022. https://arxiv.org/abs/2210.01363

  24. Sayed, T., Tarko, A., Trivedi, M. (eds.): Accident Analysis & Prevention, vol. 148–161, 2020–2021, special Issue: Crash Precursors. https://www.sciencedirect.com/journal/accident-analysis-and-prevention/special-issue/101VCPS49HX

  25. Perkins, S.R.: Traffic conflicts technique procedures manual. General Motors Research Publication, Warren, MI, Technical report GMR-896, August 1969

    Google Scholar 

  26. Wishart, J., et al.: Driving safety performance assessment metrics for ADS-equipped vehicles. SAE Technical Paper, vol. 2, no. 2020-01-1206 (2020). https://www.researchgate.net/publication/340652968_Driving_Safety_Performance_Assessment_Metrics_for_ADS-Equipped_Vehicles

  27. Heinrich, H.W.: Industrial Accident Prevention. McGraw-Hill Book Company Inc, A Scientific Approach. New York & London (1931)

    Google Scholar 

  28. Busch, C.: Preventing Industrial Accidents: Reappraising HW Heinrich-More than Triangles and Dominoes. Routledge (2021). https://www.routledge.com/Preventing-Industrial-Accidents-Reappraising-H-W-Heinrich-More-than/Busch/p/book/9780367343804

  29. Van der Schaaf, T.W., Lucas, D.A., Hale, A.R.: Near miss reporting as a safety tool. Butterworth-Heinemann. Elsevier Ltd., Oxford (1991). https://www.elsevier.com/books/near-miss-reporting-as-a-safety-tool/van-der-schaaf/978-0-7506-1178-7

  30. Aviation safety reporting system. Wikipedia, Last updated: 19-FEb-2022. https://en.wikipedia.org/wiki/Aviation_Safety_Reporting_System. Accessed 10 Apr 2022

  31. Phimister, J.R., Oktem, U., Kleindorfer, P.R., Kunreuther, H.: Near-miss incident management in the chemical process industry. Risk Anal. Int. J. 23(3), 445–459 (2003). https://riskcenter.wharton.upenn.edu/wp-content/uploads/2014/07/03-01-JP.pdf

  32. Dingus, T.A., et al.: The 100-car naturalistic driving study, Phase II-results of the 100-car field experiment," United States. Department of Transportation. National Highway Traffic Safety Administration, Technical report DOT-HS-810-593, April 2006. https://rosap.ntl.bts.gov/view/dot/37370/dot_37370_DS1.pdf

  33. Rates of motor vehicle crashes, injuries and deaths in relation to driver age, united states, 2014–2015.AAA Foundation for Traffic Safety, June 2017. https://aaafoundation.org/rates-motor-vehicle-crashes-injuries-deaths-relation-driver-age-united-states-2014-2015/

  34. Campbell, R.E., King, L.E.: The traffic conflicts technique applied to rural intersections. Accid. Anal. Prevent. 2(3), 209–221 (1970). https://www.sciencedirect.com/science/article/abs/pii/0001457570900436

  35. van der Horst, R.A.: The analysis of traffic behaviour by video. In: Kraay, J.H. (ed.) Proceedings of the 3rd International Workshop on Traffic Conflicts Techniques, no. R-82-27. Leidschendam, The Netherlands: The International Committee on Traffic Conflicts Techniques ICTCT, April 1982, pp. 26–41. https://www.ictct.net/wp-content/uploads/XX-Leidschendam-1982/1982-Proceedings.pdf#page=26

  36. Brown, T.L.: Adjusted minimum time-to-collision (TTC): a robust approach to evaluating crash scenarios. In: Proceedings of the Driving Simulation Conference North America, vol. 40, 2005, pp. 40–48. https://www.nads-sc.uiowa.edu/dscna/2005/papers/Adjusted_Minimum_TimeToCollision.pdf

  37. van der Horst, R.A.: Video analysis of road user behaviour at intersections. In: Van der Schaaf, T.W., Lucas, D.A., Hale, A.R. (eds.) Near Miss Reporting as a Safety Tool, pp. 93–109. Butterworth-Heinemann, Oxford (2013), ch. 9. https://www.elsevier.com/books/near-miss-reporting-as-a-safety-tool/van-der-schaaf/978-0-7506-1178-7

  38. Saffarzadeh, M., Nadimi, N., Naseralavi, S., Mamdoohi, A.R.: A general formulation for time-to-collision safety indicator. In: Proceedings of the Institution of Civil Engineers-Transport, vol. 166, no. 5. Thomas Telford Ltd, 2013, pp. 294–304. https://www.icevirtuallibrary.com/doi/abs/10.1680/tran.11.00031

  39. van der Horst, R.A., de Goede, M., Hair-Buijssen, S., Methorst, R.: Traffic conflicts on bicycle paths: a systematic observation of behaviour from video. Accid. Anal. Prevent. 62, 358–368 (2014). https://www.sciencedirect.com/science/article/abs/pii/S0001457513001401

  40. Every, J.L., Martin, J., Barickman, F., Rao, S., Schnelle, S., Weng, B.: A method for evaluating automated vehicle safety. SAE Government Industry Meeting (2017). https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/sae2017jevery.pdf

  41. Reporting disengagement of autonomous mode. California Code of Regulations, Title 13. Motor Vehicles, Division 1. Department of Motor Vehicles, Chapter 1. Department of Motor Vehicles, Article 3.7. Testing of Autonomous Vehicles, 2021, 13 CCR §227.50, current through 2/17/23 Register 2023, No. 7. https://govt.westlaw.com/calregs/Document/I5C2092835A1E11EC8227000D3A7C4BC3

  42. Khattak, Z.H., Fontaine, M.D., Smith, B.L.: Exploratory investigation of disengagements and crashes in autonomous vehicles under mixed traffic: an endogenous switching regime framework. IEEE Trans. Intell. Transp. Syst. 22(12), 7485–7495 (2020). https://www.osti.gov/servlets/purl/1649156

  43. Antonsson, E.K.: A general measure of collision hazard in traffic (2022). https://arxiv.org/abs/2205.08640

  44. Stevens, S.S.: On the theory of scales of measurement. Science 103(2684), 677–680 (1946). http://www.jstor.org/stable/1671815

  45. Measurement scale. Encyclopædia Britannica. https://www.britannica.com/topic/measurement-scale. Accessed 10 Apr 2022

  46. IEEE VT/ITS/AV Decision Making Working Group. White paper-literature review on kinematic properties of road users for use on safety-related models for automated driving systems. IEEE, Technical report (2022). https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9763462

  47. Intelligent Transportation Systems Committee. IEEE standard for assumptions in safety-related models for automated driving systems. IEEE Std 2846–2022 (2022). https://standards.ieee.org/ieee/2846/10831/

Download references

Author information

Authors and Affiliations

  1. Streetscope, Inc., 130 W. Union St., Pasadena, CA, 91103, USA

    Erik K. Antonsson

Authors
  1. Erik K. Antonsson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik K. Antonsson .

Editor information

Editors and Affiliations

  1. VDI/VDE Innovation + Technik GmbH, Berlin, Germany

    Gereon Meyer

  2. Stanford University, Palo Alto, CA, USA

    Sven Beiker

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Antonsson, E.K. (2023). Surrogate Measures of Automated Vehicle Safety. In: Meyer, G., Beiker, S. (eds) Road Vehicle Automation 10. ARTSymposium 2022. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-031-34757-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34757-3_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34756-6

  • Online ISBN: 978-3-031-34757-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation