403
Views
0
CrossRef citations to date
0
Altmetric
Technical Note

Safety risk assessment for autonomous vehicle road testing

, , &
Pages 652-661 | Received 15 Jun 2023, Accepted 07 Jul 2023, Published online: 24 Jul 2023

References

  • Bernhard J, Hart P, Sahu A, Scholler C, Cancimance MG. 2022. Risk-based safety envelopes for autonomous vehicles under perception uncertainty. Paper presented at: 33rd IEEE Intelligent Vehicles Symposium (IV), p. 104–111. doi:10.1109/iv51971.2022.9827199.
  • Bijlsma T, Buriachevskyi A, Frigerio A, Fu Y, Goossens K, Ors A, O, van der Perk PJ, Terechko A, Vermeulen B. 2020. A distributed safety mechanism using middleware and hypervisors for autonomous vehicles. Paper presented at: Design, Automation and Test in Europe Conference and Exhibition (DATE). p. 1175–1180.
  • Chae H, Yi K. 2020. Virtual target-based overtaking decision, motion planning, and control of autonomous vehicles. IEEE Access. 8:51363–51376. doi:10.1109/ACCESS.2020.2980391.
  • Cheng Y, Liu Z, Gao L, Zhao Y, Gao T. 2020. Traffic risk environment impact analysis and complexity assessment of autonomous vehicles based on the potential field method. Int J Environ Res Public Health. 19(16):10337. doi:10.3390/ijerph191610337.
  • De Gelder E, Op den Camp O. 2023. How certain are we that our automated driving system is safe? Traffic Inj Prev. 24(sup1):S131–S140. doi:10.1080/15389588.2023.2186733.
  • Demissie S, LaValley MP, Horton NJ, Glynn RJ, Cupples LA. 2003. Bias due to missing exposure data using complete-case analysis in the proportional hazards regression model. Stat Med. 22(4):545–557. doi:10.1002/sim.1340.
  • Favaro F, Eurich S, Nader N. 2018. Autonomous vehicles’ disengagements: trends, triggers, and regulatory limitations. Accid Anal Prev. 110:136–148. doi:10.1016/j.aap.2017.11.001.
  • He Z, Zheng L, Lu L, Guan W. 2018. Erasing lane changes from roads: a design of future road intersections. IEEE Trans Intell Veh. 3(2):173–184. doi:10.1109/TIV.2018.2804164.
  • Hernandez DC, Filonenko A, Hariyono J, Shahbaz A, Jo KH. 2016. Laser based collision warning system for high conflict vehicle-pedestrian zones. Paper presented at: 25th IEEE International Symposium on Industrial Electronics (ISIE). p. 935–939.
  • Hong A, Igharoro O, Liu Y, Niroui F, Nejat G, Benhabib B. 2019. Investigating human-robot teams for learning-based semi-autonomous control in urban search and rescue environments. J Intell Robot Syst. 94(3–4):669–686. doi:10.1007/s10846-018-0899-0.
  • IRAP. 2022. International road assessment programme methodology. https://irap.org/methodology/.
  • James J. 2022. Autonomous Cars. Robotaxis & Sensors 2022–2042. https://www.idtechex.com/en/research-report/autonomous-cars-robotaxis-and-sensors-2022-2042/832.
  • Kalra N, Paddock SM. 2016. Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transp Res Part A Policy Pract. 94:182–193. doi:10.1016/j.tra.2016.09.010.
  • Koopman P, Fratrik F. 2019. How many operational design domains, objects, and events? Paper presented at: National Conference on Artificial Intelligence. https://ceur-ws.org/Vol-2301/paper_6.pdf.
  • Krugel S, Uhl M. 2022. Autonomous vehicles and moral judgments under risk. Transp Res Part A Policy Pract. 155:1–10. doi:10.1016/j.tra.2021.10.016.
  • Li B, Song D, Li H, Pike A, Carlson P. 2018. Lane marking quality assessment for autonomous driving. Paper presented at. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). p. 8443–8448.
  • Li G, Lin S, Li S, Qu X. 2022. Learning automated driving in complex intersection scenarios based on camera sensors: a deep reinforcement learning approach. IEEE Sensors J. 22(5):4687–4696. doi:10.1109/JSEN.2022.3146307.
  • Li N, Yao Y, Kolmanovsky I, Atkins E, Girard AR. 2022. Game-theoretic modeling of multi-vehicle interactions at uncontrolled intersections. IEEE Trans Intell Transport Syst. 23(2):1428–1442. doi:10.1109/TITS.2020.3026160.
  • Luo J, Li S, Li H, Xia F. 2022. Intelligent network vehicle driving risk field modeling and path planning for autonomous obstacle avoidance. Paper presented at: the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science. 236: 8621–8634. doi:10.1177/09544062221085886.
  • Lv C, Cao D, Zhao Y, Auger DJ, Sullman M, Wang H, Dutka LM, Skrypchuk L, Mouzakitis A. 2018. Analysis of autopilot disengagements occurring during autonomous vehicle testing. IEEE/CAA J Autom Sinica. 5(1):58–68. doi:10.1109/JAS.2017.7510745.
  • Lyu Y, Luo W, Dolan J. 2021. Probabilistic safety-assured adaptive merging control for autonomous vehicles. Paper presented at: IEEE International Conference on Robotics and Automation (ICRA). p. 10764–10770. doi:10.1109/icra48506.2021.9561894.
  • Morris A, Haworth N, Filtness A, Nguatem DPA, Brown L, Rakotonirainy A, Glaser S. 2021. Autonomous vehicles and vulnerable road-users-important considerations and requirements based on crash data from two countries. Behav Sci. 11:101. doi:10.3390/bs11070101.
  • Mozaffari S, Al-Jarrah OY, Dianati M, Jennings P, Mouzakitis A. 2022. Deep learning-based vehicle behavior prediction for autonomous driving applications: a review. IEEE Trans Intell Transport Syst. 23(1):33–47. doi:10.1109/TITS.2020.3012034.
  • Notomista G, Wang M, Mac S, Egerstedt M. 2020. Enhancing game-theoretic autonomous car racing using control barrier functions. Paper presented at: IEEE International Conference on Robotics and Automation (ICRA). Electr Network. p. 5393–5399.
  • Pham M, Xiong KQ. 2021. A survey on security attacks and defense techniques for connected and autonomous vehicles. Computers & Security. 109:102269. doi:10.1016/j.cose.2021.102269.
  • Pilla RS, Lindsay BG. 2001. Alternative EM methods for nonparametric finite mixture models. Biometrika. 88(2):535–550. doi:10.1093/biomet/88.2.535.
  • Ryan C, Murphy F, Mullins M. 2021. End-to-end autonomous driving risk analysis: a behavioural anomaly detection approach. IEEE Trans Intell Transport Syst. 22(3):1650–1662. doi:10.1109/TITS.2020.2975043.
  • Sankar GS, Han K. 2020. Adaptive robust game-theoretic decision making strategy for autonomous vehicles in highway. IEEE Trans Veh Technol. 69(12):14484–14493. doi:10.1109/TVT.2020.3041152.
  • Schwarting W, Alonso-Mora J, Rus D. 2018. Planning and decision-making for autonomous vehicles. Annu Rev Control Robot Auton Syst. 1(1):187–210. doi:10.1146/annurev-control-060117-105157.
  • Soteropoulos A, Mitteregger M, Berger M, Zwirchmayr J. 2020. Automated drivability: toward an assessment of the spatial deployment of level 4 automated vehicles. Transp Res Part A Policy Pract. 136:64–84. doi:10.1016/j.tra.2020.03.024.
  • Tak S, Kim S, Yeo H. 2015. Development of a deceleration-based surrogate safety measure for rear-end collision risk. IEEE Trans Intell Transport Syst. 16(5):2435–2445. doi:10.1109/TITS.2015.2409374.
  • Tipaldi M, Glielmo L. 2015. A Markovian based approach for autonomous space systems. Paper presented at: 2nd IEEE International Workshop on Metrology for Aerospace. p. 426–430.
  • Tran DQ, Bae SH. 2021. Improved responsibility-sensitive safety algorithm through a partially observable markov decision process framework for automated driving behavior at non-signalized intersection. IntJ Automot Technol. 22(2):301–314. doi:10.1007/s12239-021-0029-z.
  • Tu H, Cui H, Lu C, Li H, Liu J, Hou D. 2020. A risk-avoiding disengagement frequency model for assessing driving ability of autonomous vehicles in road testing. J Tongji University. Nat Sci. 48:1562–1569.
  • Tu H, Liu F, Cui H, Bao S, Zhao Y, Cao Y, Cao J. 2021. Empirical data-driven identification of driving modes in autonomous vehicle road testing. China J Highw Transp. 34:231–239.
  • Van Wyk F, Khojandi A, Masoud N. 2019. A path towards understanding factors affecting crash severity in autonomous vehicles using current naturalistic driving data. Paper presented at: Intelligent Systems Conference (IntelliSys). p. 106–120. doi:10.1007/978-3-030-29513-4_8.
  • Veres SM, Molnar L, Lincoln NK, Morice CP. 2011. Autonomous vehicle control systems – a review of decision making. Paper presented at: The Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering. 225: p. 155–195. doi:10.1177/2041304110394727.
  • Wang J, Zhang L, Huang Y, Zhao J, Bella F. 2020. Safety of autonomous vehicles. J Adv Transp. 2020. 2020:1–13. doi:10.1155/2020/8867757.
  • Wang S, Li Z. 2019. Exploring causes and effects of automated vehicle disengagement using statistical modeling and classification tree based on field test data. Accid Anal Prev. 129:44–54. doi:10.1016/j.aap.2019.04.015.
  • Weber H, Bock J, Klimke J, Roesener C, Hiller J, Krajewski R, Zlocki A, Eckstein L. 2019. A framework for definition of logical scenarios for safety assurance of automated driving. Traffic Inj Prev. 20(sup1):S65–S70. doi:10.1080/15389588.2019.1630827.
  • Zhang T, Tang C, Smith G, Wu L. 2014. Road assessment model and pilot application in China. Discrete Dyn Nat Soc. 2014:1–7. doi:10.1155/2014/823057.
  • Zhao J, Deng W, Wang J. 2011. Bayesian network-based urban road traffic accidents analysis. J Southeast Univ. Nat Sci Ed. 41:1300–1306.
  • Zou X, Yue W. 2017. A bayesian network approach to causation analysis of road accidents using Netica. J Adv Transp. 2017:1–18. doi:10.1155/2017/2525481.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.