Skip to main content

Advertisement

SpringerLink
Log in

Verifying Collision Risk Estimation using Autonomous Driving Scenarios Derived from a Formal Model

  • Regular paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Autonomous driving technology is safety-critical and thus requires thorough validation. In particular, the probabilistic algorithms employed in perception systems of autonomous vehicles (AV) are notoriously hard to validate due to the wide range of possible critical scenarios. Such critical scenarios cannot be easily addressed with current manual validation methods, thus there is a need for an automatic and formal validation technique. To this end, we propose a new approach for perception component verification that, given a high-level and human-interpretable description of a critical situation, generates relevant AV scenarios and uses them for automatic verification. To achieve this goal, we integrate two recently proposed methods for the generation and the verification that are based on formal verification tools. First, we use formal conformance test generation tools to derive, from a verified formal model, sets of scenarios to be run in a simulator. Second, we model check the traces of the simulation runs to validate the probabilistic estimation of collision risks. Using formal methods brings the combined advantages of an increased confidence in the correct representation of the chosen configuration (temporal logic verification), a guarantee of the coverage and relevance of automatically generated scenarios (conformance testing), and an automatic quantitative analysis of the test execution (verification and statistical analysis on traces).

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Code Availability

The data generated and/or analyzed during the current study are available from the corresponding authors on reasonable request. The generic parts of the LNT model [53] are available from the MARS model repository.Footnote 1

Notes

  1. http://mars-workshop.org/repository/031-AutoVehicles.html

References

  1. Boudette, N.: ‘It happened so fast’: inside a fatal tesla autopilot accident. https://www.nytimes.com/2021/08/17/business/tesla-autopilot-accident.html (2021)

  2. Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. A Policy Pract. 77, 167–181 (2015)

    Article  Google Scholar 

  3. Redmond, A.M.: A critical review of photonic opportunities within autonomous vehicles transport system. In: Proceedings of the 6th international forum on research and technology for society and industry (RTSI’2021), Naples, Italy, pp. 188–193. IEEE. https://doi.org/10.1109/RTSI50628.2021.9597361 (2021)

  4. McCarthy, J., Colburn, T.R., Fetzer, J.H., Rankin, T.L.: Towards a mathematical science of computation, pp. 35–56. Springer. https://doi.org/10.1007/978-94-011-1793-7_2 (1993)

  5. Garavel, H., Graf, S.: Formal methods for safe and secure computer systems - BSI study 875 BSI german federal office for information security (2013)

  6. Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M., Dolan, J., Duggins, D., Galatali, T., Geyer, C., et al: Autonomous driving in urban environments: boss and the urban challenge. J. Field Robot. 25(8), 425–466 (2008)

    Article  Google Scholar 

  7. Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., et al: A perception-driven autonomous urban vehicle. J. Field Robot. 25(10), 727–774 (2008)

    Article  Google Scholar 

  8. Ding, W., Chen, B., Xu, M., Zhao, D.: Learning to collide: an adaptive safety-critical scenarios generating method. In: International conference on intelligent robots and systems (IROS), pp. 2243–2250. IEEE (2020)

  9. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st annual conference on robot learning, pp. 1–16 (2017)

  10. Riedmaier, S., Ponn, T., Ludwig, D., Schick, B., Diermeyer, F.: Survey on scenario-based safety assessment of automated vehicles. IEEE Access 8, 87456–87477 (2020). https://doi.org/10.1109/ACCESS.2020.2993730

    Article  Google Scholar 

  11. Bishop, P., Bloomfield, R.: A methodology for safety case development. In: Redmill, F., Anderson, T. (eds.) Proceedings of the sixth safety-critical systems symposium on industrial perspectives of safety-critical systems, Birmingham, UK, pp 194–203. Springer. https://doi.org/10.1007/978-1-4471-1534-2_14 (1998)

  12. Ledent, P., Paigwar, A., Renzaglia, A., Mateescu, R., Laugier, C.: Formal validation of probabilistic collision risk estimation for autonomous driving. In: CIS-RAM 2019 - 9th IEEE international conference on cybernetics and intelligent systems (CIS) robotics, automation and mechatronics (RAM), pp. 1–6. IEEE. https://hal.inria.fr/hal-02355551 (2019)

  13. Horel, J.-B., Laugier, C., Marsso, L., Mateescu, R., Muller, L., Paigwar, A., Renzaglia, A., Serwe, W.: Using formal conformance testing to generate scenarios for autonomous vehicles. In: DATE/ASD 2022 - design, automation and test in europe - autonomous systems design. IEEE, Antwerp, Belgium. https://hal.inria.fr/hal-03516799(2022)

  14. Jard, C., Jéron, T.: TGV: theory, principles and algorithms – a tool for the automatic synthesis of conformance test cases for non-deterministic reactive systems. Springer Int. J. Softw. Tools Technol. Transfer (STTT) 7(4), 297–315 (2005)

    Article  Google Scholar 

  15. Garavel, H., Lang, F., Mateescu, R., Serwe, W.: CADP 2011: a toolbox for the construction and analysis of distributed processes. Springer Int. J. Softw .Tools Technol. Transfer (STTT) 15(2), 89–107 (2013)

    Article  MATH  Google Scholar 

  16. Garavel, H., Lang, F., Serwe, W.: From LOTOS to LNT. In: Modeled, tested, trusted – essays dedicated to Ed Brinksma on the occasion of his 60th birthday. LNCS, vol. 10500, pp. 3–26. Springer. https://doi.org/10.1007/978-3-319-68270-9_1 (2017)

  17. Mateescu, R., Thivolle, D.: A model checking language for concurrent value-passing systems. In: Cuéllar, J., Maibaum, T.S.E., Sere, K. (eds.) Proceedings of the 15th international symposium on formal methods (FM’08), Turku, Finland. Lecture notes in computer science, vol. 5014, pp. 148–164. Springer. https://doi.org/10.1007/978-3-540-68237-0_12 (2008)

  18. Mateescu, R., Garavel, H.: XTL: a meta-language and tool for temporal logic model-checking. In: Margaria, T. (ed.) Proceedings of the international workshop on software tools for technology transfer (STTT’98), Aalborg, Denmark, pp. 33–42. BRICS (1998)

  19. Marsso, L., Mateescu, R., Serwe, W.: TESTOR: a modular tool for on-the-fly conformance test case generation. In: 24th Int. conference on tools and algorithms for the construction and analysis of systems (TACAS’18). LNCS, vol. 10806, pp. 211–228. Springer. https://doi.org/10.1007/978-3-319-89963-3_13 (2018)

  20. Marsso, L., Mateescu, R., Serwe, W.: Automated Transition Coverage in Behavioural Conformance Testing. In: 32nd IFIP int. conference on testing software and systems (ICTSS’20), Naples, Italy, pp. 219–235. Springer. https://doi.org/10.1007/978-3-030-64881-7_14 (2020)

  21. Rummelhard, L., Négre, A., Laugier, C.: Conditional monte carlo dense occupancy tracker. In: IEEE 18th international conference on intelligent transportation systems, pp. 2485–2490 (2015)

  22. Grolemund, G., Wickham, H.: R for data science o’reilly media (2016)

  23. Tuncali, C.E., Pavlic, T.P., Fainekos, G.E.: Utilizing S-Taliro as an Automatic Test Generation Framework for Autonomous Vehicles. In: 19th IEEE international conference on intelligent transportation systems (ITSC), Rio De Janeiro, Brazil, pp. 1470–1475. https://doi.org/10.1109/ITSC.2016.7795751 (2016)

  24. Gangopadhyay, B., Khastgir, S., Dey, S., Dasgupta, P., Montana, G., Jennings, P.A.: Identification of test cases for automated driving systems using bayesian optimization. In: 22nd IEEE intelligent transportation systems conference (ITSC), Auckland, New Zealand, pp. 1961–1967. https://doi.org/10.1109/ITSC.2019.8917103 (2019)

  25. Khastgir, S., Dhadyalla, G., Birrell, S., Redmond, S., Addinall, R., Jennings, P.: Test scenario generation for driving simulators using constrained randomization technique. Technical report, SAE technical paper (2017)

  26. Klischat, M., Althoff, M.: Generating critical test scenarios for automated vehicles with evolutionary algorithms. In: IEEE intelligent vehicles symposium (IV), pp. 2352–2358. https://doi.org/10.1109/IVS.2019.8814230 (2019)

  27. Althoff, M., Lutz, S.: Automatic generation of safety-critical test scenarios for collision avoidance of road vehicles. IEEE Intell Vehicles Symp (IV), pp. 1326–1333 (2018)

  28. Krajewski, R., Moers, T., Nerger, D., Eckstein, L.: Data-driven maneuver modeling using generative adversarial networks and variational autoencoders for safety validation of highly automated vehicles. In: Zhang, W., Bayen, A.M., Medina, J.J.S., Barth, M.J. (eds.) 21st IEEE international conference on intelligent transportation systems (ITSC), Maui, HI, USA, pp. 2383–2390. https://doi.org/10.1109/ITSC.2018.8569971 (2018)

  29. Li, Y., Tao, J., Wotawa, F.: Ontology-based test generation for automated and autonomous driving functions. Inf. Softw. Technol. 117, 106200 (2020)

    Article  Google Scholar 

  30. Singh, V., Pitale, M.: Impact of automotive system safety design on machine learning based perception systems. In: Proceedings of the 4th IEEE international conference on industrial cyber-physical systems, (ICPS’2021), Victoria, BC, Canada, pp. 591–596. https://doi.org/10.1109/ICPS49255.2021.9468225 (2021)

  31. Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the 29th IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA, pp. 779–788. https://doi.org/10.1109/CVPR.2016.91 (2016)

  32. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Proceedings of the 14th European conference on computer vision (ECCV’2016), Amsterdam, The Netherlands. Lecture notes in computer science, vol. 9905, pp. 21–37. Springer. https://doi.org/10.1007/978-3-319-46448-0_2 (2016)

  33. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster r-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  34. Zhou, Y., Tuzel, O.: Voxelnet: end-to-end learning for point cloud based 3d object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4490–4499 (2018)

  35. Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: fast encoders for object detection from point clouds. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12697–12705 (2019)

  36. Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., Li, H.: PV-RCNN: point-voxel feature set abstraction for 3d object detection. In: Proceedings of the 2020 IEEE/CVF international conference on computer vision and pattern recognition (CVPR), Seattle, WA, USA, pp. 10526–10535. https://doi.org/10.1109/CVPR42600.2020.01054 (2020)

  37. Wicker, M., Huang, X., Kwiatkowska, M.: Feature-guided black-box safety testing of deep neural networks. In: Proceedings of the 24th international conference on tools and algorithms for the construction and analysis of systems (TACAS’2018), Thessaloniki, Greece. Lecture notes in computer science, vol. 10805, pp. 408–426. Springer. https://doi.org/10.1007/978-3-319-89960-2_22 (2018)

  38. Melis, M., Demontis, A., Biggio, B., Brown, G., Fumera, G., Roli, F.: Is deep learning safe for robot vision? adversarial examples against the icub humanoid. In: Proceedings of the IEEE international conference on computer vision workshops, pp. 751–759 (2017)

  39. Serban, A., Poll, E., Visser, J.: Adversarial examples on object recognition: a comprehensive survey. ACM Comput. Surveys (CSUR) 53(3), 1–38 (2020)

    Article  Google Scholar 

  40. Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: Majumdar, R., Kuncak, V. (eds.) Proceedings of the 29th international conference on computer aided verification (CAV’2017), Heidelberg, Germany. Lecture notes in computer science, vol. 10426, pp. 3–29. Springer. https://doi.org/10.1007/978-3-319-63387-9_1 (2017)

  41. Shekar, A.K., Gou, L., Ren, L., Wendt, A.: Label-free robustness estimation of object detection cnns for autonomous driving applications. Int. J. Comput. Vis. 129(4), 1185–1201 (2021). https://doi.org/10.1007/s11263-020-01423-x

    Article  Google Scholar 

  42. Wu, W., Xu, H., Zhong, S., Lyu, M.R., King, I.: Deep validation: toward detecting real-world corner cases for deep neural networks. In: Proceedings of the 49th annual IEEE/IFIP international conference on dependable systems and networks (DSN’2019), Portland, OR, USA, pp. 125–137. https://doi.org/10.1109/DSN.2019.00026 (2019)

  43. Zhang, M., Zhang, Y., Zhang, L., Liu, C., Khurshid, S.: Deeproad: gan-based metamorphic testing and input validation framework for autonomous driving systems. In: Proceedings of the 33rd ACM/IEEE international conference on automated software engineering (ASE’2018), Montpellier, France, pp. 132–142. https://doi.org/10.1145/3238147.3238187 (2018)

  44. Hu, B.C., Marsso, L., Czarnecki, K., Salay, R., Shen, H., Chechik, M.: If a human can see it, so should your system: reliability requirements for machine vision components. In: Proceedings of the 44th international conference on software engineering (ICSE’22), Pittsburgh, PA, USA. ACM (2022)

  45. Zhao, X., Robu, V., Flynn, D., Dinmohammadi, F., Fisher, M., Webster, M.: Probabilistic model checking of robots deployed in extreme environments. arXiv:1812.04128 (2018)

  46. Calinescu, R., Ghezzi, C., Johnson, K., Pezzé, M., Rafiq, Y., Tamburrelli, G.: Formal verification with confidence intervals to establish quality of service properties of software systems. IEEE Trans. Reliability 65(1), 107–125 (2016)

    Article  Google Scholar 

  47. Barbier, M., Renzaglia, A., Quilbeuf, J., Rummelhard, L., Paigwar, A., Laugier, C., Legay, A., Ibañez-Guzmán, J., Simonin, O.: Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking. In: IEEE intelligent vehicles symposium (IV), Paris, France, pp. 252–259. https://doi.org/10.1109/IVS.2019.8813793 (2019)

  48. Paigwar, A., Baranov, E., Renzaglia, A., Laugier, C., Legay, A.: Probabilistic collision risk estimation for autonomous driving: validation via statistical model checking. In: IEEE intelligent vehicles symposium (IV), Las Vegas, NV, USA, pp. 737–743. https://doi.org/10.1109/IV47402.2020.9304821 (2020)

  49. Champelovier, D., Clerc, X., Garavel, H., Guerte, Y., McKinty, C., Powazny, V., Lang, F., Serwe, W., Smeding G.: Reference manual of the LNT to LOTOS translator (version 7.0). INRIA, Grenoble (2021)

  50. Tretmans, J.: Testing concurrent systems: a formal approach. In: Baeten, J.C.M., Mauw, S. (eds.) Proceedings of the 10th international conference on concurrency theory (CONCUR’99), Eindhoven, The Netherlands. Lecture notes in computer science, vol. 1664, pp. 46–65. Springer. https://doi.org/10.1007/3-540-48320-9_6 (1999)

  51. Charlesworth, A.: The multiway rendezvous. ACM Trans. Program. Lang. Syst. 9(3), 350–366 (1987). https://doi.org/10.1145/24039.24050

    Article  MATH  Google Scholar 

  52. Garavel, H., Serwe, W.: The unheralded value of the multiway rendezvous: illustration with the production cell benchmark. In: 2nd Workshop on models for formal analysis of real systems (MARS’17). EPTCS, vol. 244, pp. 230–270. https://doi.org/10.4204/EPTCS.244.10 (2017)

  53. Marsso, L., Mateescu, R., Muller, L., Serwe, W.: Formally modeling autonomous vehicles in lnt for simulation and testing. In: Proceedings of the 5th workshop on models for formal analysis of real systems (MARS@ETAPS’2022), Munich, Germany. EPTCS (2022)

  54. Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (2001)

    Book  MATH  Google Scholar 

  55. Chang, E., Manna, Z., Pnueli, A.: Characterization of temporal property classes. In: Proceedings of the 19th ICALP (Vienna). Lecture notes in computer science, vol. 623, pp. 474–486. Springer (1992)

  56. Marsso, L., Mateescu, R., Parissis, I., Serwe, W.: Asynchronous testing of synchronous components in GALS systems. In: Proceedings of the 15th international conference on integrated formal methods (IFM’2019), Bergen, Norway. LNS, vol. 11918, pp. 360–378. Springer. https://doi.org/10.1007/978-3-030-34968-4_20 (2019)

  57. Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)

    Article  Google Scholar 

  58. Moravec, H.: Sensor fusion in certainty grids for mobile robots. AI Mag. 9(2), 61 (1988)

    Google Scholar 

  59. Fei, J., Peng, K., Heidenreich, P., Bieder, F., Stiller, C.: Pillarsegnet: pillar-based semantic grid map estimation using sparse lidar data. In: 2021 IEEE intelligent vehicles symposium (IV), pp. 838–844. IEEE (2021)

  60. Saha, A., Mendez, O., Russell, C., Bowden, R.: Translating images into Maps. In: 2022 International conference on robotics and automation (ICRA), pp. 9200–9206. https://doi.org/10.1109/ICRA46639.2022.9811901 (2022)

  61. Philion, J., Fidler, S.: Lift, splat, shoot: encoding images from arbitrary camera rigs by implicitly unprojecting to 3d. In: European conference on computer vision, pp. 194–210. Springer (2020)

  62. Zhou, T., Yang, M., Jiang, K., Wong, H., Yang, D.: Mmw radar-based technologies in autonomous driving: a review sensors, vol. 20(24). https://doi.org/10.3390/s20247283 (2020)

  63. Hendy, N., Sloan, C., Tian, F., Duan, P., Charchut, N., Xie, Y., Wang, C., Philbin, J.: Fishing net: future inference of semantic heatmaps in grids. arXiv:2006.09917 (2020)

  64. Rummelhard, L., Nègre, A., Perrollaz, M., Laugier, C.: Probabilistic grid-based collision risk prediction for driving application. In: Springer (Ed.) international synposium on experimental robotics (2014)

  65. Kaempchen, N., Schiele, B., Dietmayer, K.: Situation assessment of an autonomous emergency brake for arbitrary vehicle-to-vehicle collision scenarios. IEEE Trans. Intell. Transport. Syst., vol. 10(4) (2009)

  66. Garavel, H.: Binary coded graphs: definition of the bcg format. Rapport SPECTRE C28, Laboratoire de Génie Informatique – Institut IMAG, Grenoble (1991)

  67. Alpern, B.B., Schneider, F.: Verifying temporal properties without temporal logic. ACM Trans. Programm. Lang. Syst. (TOPLAS) 11, 147–167 (2001). https://doi.org/10.1145/59287.62028

    Article  MATH  Google Scholar 

  68. Ledoux, V., Krishnakumar, R., Hervé, V.: Livrable L2.8 situations d’interactions accidentogènes : enjeux. financé par la FSR et la DSR. https://surca.univ-gustave-eiffel.fr/livrables-et-publications/wp2-etat-de-lart-donnees-accidentologiques/(2019)

  69. Lefèvre, S., Vasquez, D., Laugier, C.: A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J., vol 1(1). https://doi.org/10.1186/s40648-014-0001-z (2014)

  70. Bartocci, E., Falcone, Y., Francalanza, A., Reger, G.: Introduction to runtime verification. In: Lectures on runtime verification - introductory and advanced topics. Lecture notes in computer science, vol. 10457, pp. 1–33. Springer. https://doi.org/10.1007/978-3-319-75632-5_1 (2018)

  71. Bagschik, G., Menzel, T., Maurer, M.: Ontology based scene creation for the development of automated vehicles. In: IEEE intelligent vehicles symposium (IV), pp. 1813–1820. https://doi.org/10.1109/IVS.2018.8500632 (2018)

  72. Makartetskiy, D., Marchetto, G., Sisto, R., Valenza, F., Virgilio, M., Leri, D., Denti, P., Finizio, R.: (User-friendly) formal requirements verification in the context of ISO26262. Eng. Sci. Technol. Int. J. 23, 494–506 (2020). https://doi.org/10.1016/j.jestch.2019.09.005

    Google Scholar 

Download references

Funding

A part of this work was supported by the project ArchitectECA2030 that has been accepted for funding within the Electronic Components and Systems for European Leadership Joint Undertaking in collaboration with the European Union’s H2020 Framework Programme (H2020/2014-2020) and National Authorities, under grant agreement No. 877539. A part of this work was supported by the PRISSMA project, co-financed by the French Grand Défi on Trustworthy AI for Industry.

Author information

Authors and Affiliations

  1. Univ. Grenoble Alpes, Inria, 38000, Grenoble, France

    Jean-Baptiste Horel, Christian Laugier & Anshul Paigwar

  2. Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, 38000, Grenoble, France

    Philippe Ledent, Lucie Muller, Radu Mateescu & Wendelin Serwe

  3. Department of Computer Science, University of Toronto, Toronto, Ontario, M5S 2E4, Canada

    Lina Marsso

  4. Univ. Lyon, Inria, INSA Lyon, CITI, 69100, Villeurbanne, France

    Alessandro Renzaglia

Authors
  1. Jean-Baptiste Horel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philippe Ledent
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lina Marsso
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lucie Muller
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christian Laugier
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Radu Mateescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Anshul Paigwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alessandro Renzaglia
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wendelin Serwe
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jean-Baptiste Horel contributed to the generation of behavior trees from test cases, the execution of behavior trees in CARLA simulator, the generation of traces of the CMCDOT, and writing the manuscript.

Philippe Ledent proposed, developed, and executed the trace verification and statistical analysis.

Lina Marsso contributed to the design of the formal LNT model, the formalization of the properties in temporal logic, the conformance test generation, the manuscript writing, and the study of related work.

Lucie Muller participated in the design of the formal LNT model, the translation from CTG to test cases to JSON files, the definition and generation of scenarios, and the experimentation on these scenarios with the description of the results in the tables.

Christian Laugier was the main initiator of this paper and supervised all the work related to autonomous vehicles, perception and collision risk evaluation through the CMCDOT algorithm, of which he is one of the authors.

Radu Mateescu contributed to the formulation of the models’ correctness properties in temporal logic and to the description of the conformance testing process.

Anshul Paigwar developed ROS-bridge to integrate CARLA sensor outputs with the CMCDOT algorithm and wrote a collision data logger for logging ground truth and predictions from CMCDOT, facilitating the generation of traces.

Alessandro Renzaglia contributed to the formulation of the problem regarding the perception and collision risk estimation modules and the definition of the properties for their assessment.

Wendelin Serwe participated in the design of the formal model, the description of the model, and the scenario extraction using conformance testing techniques.

All authors proofread the final version of the article.

Corresponding authors

Correspondence to Jean-Baptiste Horel, Philippe Ledent, Lina Marsso, Lucie Muller, Radu Mateescu, Alessandro Renzaglia or Wendelin Serwe.

Ethics declarations

Conflicts of interest/Competing interests

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Horel, JB., Ledent, P., Marsso, L. et al. Verifying Collision Risk Estimation using Autonomous Driving Scenarios Derived from a Formal Model. J Intell Robot Syst 107, 59 (2023). https://doi.org/10.1007/s10846-023-01808-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-023-01808-3

Keywords

Search

Navigation