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Trustworthy Autonomous System Development

Published:18 April 2023Publication History
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Abstract

Autonomous systems emerge from the need to progressively replace human operators by autonomous agents in a wide variety of application areas. We offer an analysis of the state of the art in developing autonomous systems, focusing on design and validation and showing that the multi-faceted challenges involved go well beyond the limits of weak AI. We argue that traditional model-based techniques are defeated by the complexity of the problem, while solutions based on end-to-end machine learning fail to provide the necessary trustworthiness. We advocate a hybrid design approach, which combines the two, adopting the best of each, and seeks tradeoffs between trustworthiness and performance. We claim that traditional risk analysis and mitigation techniques fail to scale and discuss the trend of moving away from correctness at design time and toward reliance on runtime assurance techniques. We argue that simulation and testing remain the only realistic approach for global validation and show how current methods can be adapted to autonomous systems. We conclude by discussing the factors that will play a decisive role in the acceptance of autonomous systems and by highlighting the urgent need for new theoretical foundations.

REFERENCES

  1. [1] Harel David, Marron Assaf, and Sifakis Joseph. 2022. Creating a foundation for next-generation autonomous systems. IEEE Des. Test 39, 1 (2022), 4956.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Harel D., Marron Assaf, and Sifakis J.. 2020. Autonomics: In search of a foundation for next generation autonomous systems. Proc. Natl. Acad. Sci. U.S.A. 117, 30 (2020), 1749117498.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Autonomic Computing. 2006. An architectural blueprint for autonomic computing. IBM White Paper 31, (2006), 16. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=0e99837d9b1e70bb35d516e32ecfc345cd30e795.Google ScholarGoogle Scholar
  4. [4] Jordan Michael I.. Artificial Intelligence—The Revolution Hasn't Happened Yet. Retrieved from https://hdsr.mitpress.mit.edu/pub/wot7mkc1/release/9.Google ScholarGoogle Scholar
  5. [5] Harel David and Pnueli Amir. 1985. On the Eevelopment of Reactive Systems, Logics and Models of Concurrent Systems (K. R. Apt, Ed.). NATO ASI Series, F-13, Springer-Verlag, New York, 477498.Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Sifakis Joseph. 2018. Autonomous systems an architectural characterization. arXiv:1811.10277. Retrieved from https://arxiv.org/abs/1811.10277.Google ScholarGoogle Scholar
  7. [7] Efroni S., Harel D., and Cohen I. R.. 2005. Reactive animation: Realistic modeling of complex dynamic systems. Computer 38, 1, (2005), 3847. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Simon Bliudze, Sébastien Furic, Joseph Sifakis, and Antoine Viel. 2019. Antoine viel: Rigorous design of cyber-physical systems—Linking physicality and computation. Softw. Syst. Model 18, 3 (2019), 16131636.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. [9] Bloem R., Jacobs S., Khalimov A., Konnov I., Rubin S., Veith H., and Widder J.. 2015. Decidability of Parameterized Verification, Synthesis, Lectures on Distributed Computing Theory. Morgan & Claypool.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Sifakis J.. 2012. Rigorous system design. Foundations and Trends in Electronic Design Automation 6, 4 (2012), 293362.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. [11] ISO Online Browsing Platform. Road vehicles— Functional safety— Part9: Automotive Safety Integrity Level (ASIL)-oriented and safety-oriented analyses. Retrieved from https://www.iso.org/obp/ui/#iso:std:iso:26262:-9:ed-1:v1:en.Google ScholarGoogle Scholar
  12. [12] Wikipedia. V-model. Retrieved from https://en.wikipedia.org/wiki/V-model.Google ScholarGoogle Scholar
  13. [13] Agile Alliance. Agile 101. Retrieved from https://www.agilealliance.org/agile101/.Google ScholarGoogle Scholar
  14. [14] Madhavan Rajmohan, Messina Elena R., and Albus James S.. 2006. Intelligent Vehicle Systems: A 4D/RCS Approach. Nova Science.Google ScholarGoogle Scholar
  15. [15] Albus James S. and Barbera Anthony J.. 2005. RCS: A cognitive architecture for intelligent multi-agent systems. Annu. Rev. Contr. 29, 1 (2005), 8799.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Ulbrich Simon, Reschka Andreas, Rieken Jens, Ernst Susanne, Bagschik Gerrit, Dierkes Frank, Nolte Marcus, and Maurer Markus. 2017. Towards a functional system architecture for automated vehicles, arXiv:1703.08557 [cs.SY]. Retrieved from https://arxiv.org/abs/1703.08557.Google ScholarGoogle Scholar
  17. [17] Dersten Sara, Axelsson Jakob, and Fröberg Joakim. 2015. An analysis of a layered system architecture for autonomous construction vehicles. In Proceedings of the Annual IEEE Systems Conference (SysCon’15). 582588.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Braud Thomas, Ivanchev Jordan, Deboeser Corvin, Knoll Alois C., Eckhoff David, and Sangiovanni-Vincentelli Alberto L.. 2021. AVDM: A hierarchical command-and-control system architecture for cooperative autonomous vehicles in highways scenario using microscopic simulations. Auton. Agents Multi Agent Syst. 35, 1 (2021), 16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Aldrich Jonathan, Garlan David, Kästner Christian, Goues Claire Le, Mohseni-Kabir Anahita, Ruchkin Ivan, Samuel Selva, Schmerl Bradley R., Timperley Christopher Steven, Veloso Manuela, Voysey Ian, Biswas Joydeep, Guha Arjun, Holtz Jarrett, Cámara Javier, and Jamshidi Pooyan. 2019. Model-based adaptation for robotics software. IEEE Softw. 36, 2 (2019), 8390.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] VIRES Simulationstechnologie GmbH. 2006. OpenDRIVE Format Specification. Tech. Rep. V 1.4.Google ScholarGoogle Scholar
  21. [21] ASAM e.V. 2020. ASAM OpenDRIVE—Open Dynamic Road Information for Vehicle Environment. Tech. Rep. V 1.6.0. Google ScholarGoogle Scholar
  22. [22] Beetz J. and Borrmann A.. 2018. Benefits and limitations of linked data approaches for road modeling and data exchange. In Proceedings of the 25th EG-ICE International Workshop Advanced Computing Strategies for Engineering, Lecture Notes in Computer Science, Vol. 10864I. F. C. Smith and B.Domer (Eds.). Springer, 245261.Google ScholarGoogle Scholar
  23. [23] Bagschik G., Menzel T., and Maurer M.. 2018. Ontology based scene creation for the development of automated vehicles. In Proceedings of the IEEE Intelligent Vehicles Symposium (IV’18) IEEE, 18131820.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Poggenhans F., Pauls J., Janosovits J., Orf S., Naumann M., Kuhnt F., and Mayr M.. 2018. Lanelet2: A high-definition map framework for the future of automated driving. In Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC’18), W. Zhang, A. M. Bayen, J. J. S. Medina, and M. J. Barth (Eds.), 16721679.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. [25] Bozga Marius and Sifakis Joseph. 2021. Specification and validation of autonomous driving systems: A multilevel semantic framework, arXiv:2109.06478 [cs.MA]. Retrieved from https://arxiv.org/abs/2109.06478.Google ScholarGoogle Scholar
  26. [26] Laprie Jean-Claude. 1992. Dependability: Basic concepts and terminology. In Dependable Computing and Fault-Tolerant Systems. Springer, Berlin, (1992).Google ScholarGoogle Scholar
  27. [27] George Apostolakis. 2004. How useful is quantitative risk assessment? Risk Anal. 24, 3 (2004).Google ScholarGoogle Scholar
  28. [28] Lee W. S., Grosh D. L., Tillman F. A., and Lie C. H.. 1985. Fault tree analysis, methods, and applications a review. IEEE Trans. Reliabil. R-34, 3 (1985).Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Asim Abdulkhaleq, Stefan Wagner, and Nancy Leveson. 2015. A comprehensive safety engineering approach for software-intensive systems based on STPA. arXiv:1612.03109 [cs.SE]. .Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Wallace Malcolm. 2005. Modular architectural representation and analysis of fault propagation and transformation. Electr. Not. Theor. Comput. Sci. 141 (2005), 5371.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. [31] NHTSA. 2007. Pre-crash scenario typology for crash avoidance research, DOT HS 810 767.Google ScholarGoogle Scholar
  32. [32] Zolghadri A. 2012. Advanced model-based FDIR techniques for aerospace systems: Today challenges and opportunities. In Progress in Aerospace Sciences. Vol. 53, Elsevier, 1829.Google ScholarGoogle Scholar
  33. [33] Abdulkhaleq Asim, Lammering Daniel, Wagner Stefan, Rôder Jürgen, Balbierer Norbert, Ramsauer Ludwig, Rastec Thomas, and Boehmert Hagen. 2017. A systematic approach based on STPA for developing a dependable architecture for fully automated driving vehicles. Proceedings of the 4th European STAMP Workshop 179 (2017), 4151.Google ScholarGoogle Scholar
  34. [34] Schierman John D., DeVore Michael D., Richards Nathan D., Gandhi Neha, Cooper Jared K., and Horneman Kenneth R.. 2015. Runtime assurance framework development for highly adaptive flight control systems, Barron associates. AFRL-RQ-WP-TR-2016-0001Final Report. Stony Brook University.Google ScholarGoogle Scholar
  35. [35] Sha L.. 2001. Using simplicity to control complexity. IEEE Softw. 18, 4 (2001), 2028.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. [36] Mayo J. R., Armstrong R. C., Hulette G. C., Salloum M., and Smith A. M.. 2018. Robust digital computation in the physical world. In Cyber-Physical Systems Security. Springer, 121. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Althoff M., Maierhofer S., and Pek C.. 2021. Provably-correct and comfortable adaptive cruise control. IEEE Trans. Intell. Vehic. 6, 1 (2021).Google ScholarGoogle Scholar
  38. [38] Bauer Andreas, Leucker Martin, and Schallhart Christian. 2011. Runtime verification for LTL and TLTL. ACM Trans. Softw. Eng. Methodol. 20, 4 (2011), 164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. [39] Bucchiarone Antonio and Galeotti Juan P.. 2008. Dynamic software architectures verification using dynalloy. Electron. Commun. Eur. Assoc. Softw. Sci. Technol. 10 (2008). DOI:Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] WAYMO. Waymo reaches 5 million self-driven miles. February 27, 2018. Retrieved from https://blog.waymo.com/2019/08/waymo-reaches-5-million-self-driven.html.Google ScholarGoogle Scholar
  41. [41] Setty Y., Cohen I. R., Dor Y., and Harel D.. 2008. Four-dimensional realistic modeling of pancreatic organogenesis. Proc. Natl. Acad. Sci. U.S.A. 105, 51 (2008), 2037420379.Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Bloch N., Weiss G., Szekely S., and Harel D.. 2015. An interactive tool for animating biology, and its use in spatial and temporal modeling of a cancerous tumor and its microenvironment. PLoS ONE 10, 7 (2015), e0133484. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] ASAM Open. 2020. Scenario—Dynamic content in driving simulation, UML Modeling Rules. Tech. Rep. V 1.0.0, ASAM e.V.Google ScholarGoogle Scholar
  44. [44] Damm W. and Harel D.. 2001. LSCs: Breathing life into message sequence charts. Form. Methods Syst. Des. 19, 1 (2001), 4580.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Damm W., Kemper S., Môhlmann E., Peikenkamp T., and Rakow A.. 2018. Using traffic sequence charts for the development of HAVs. In Proceedings of the European Congress on Embedded Real Time Systems (ERTS’18).Google ScholarGoogle Scholar
  46. [46] Harel D. and Marelly R.. 2003. Come, let's play: Scenario-based programming using lscs and the play-engine. Springer-Verlag, Berlin.Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Fremont D. J., Kim E., Pant Y. V., Seshia S. A., Acharya A., Bruso X., Wells P., Lemke S., Lu Q., and Mehta S.. 2020. Formal scenario-based testing of autonomous vehicles: From simulation ta the real world. In Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC’20). IEEE, 18.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. [48] Fremont D. J., Kim E., Dreossi T., Ghosh S., Yue X., Sangiovanni-Vincentelli A. L., and Seshia S. A.. 2020. Scenic: A language for scenario specification and data generation. arXiv:2010.06580. Retrieved from https://arxiv.org/abs/2010.06580.Google ScholarGoogle Scholar
  49. [49] El-Hokayem Antoine, Bozga Marius, and Sifakis Joseph. 2021. A temporal configuration logic for dynamic reconfigurable systems. SAC'21. ACM, 14191428.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. [50] Klemens Esterle, Vincent Aravantinos, and Alois Knoll. 2019. From specifications to behavior: Maneuver verification in a semantic state space. In Proceedings of the IEEE Intelligent Vehicles Symposium (IV’19). IEEE, 21402147.Google ScholarGoogle Scholar
  51. [51] Rizaldi A. and Althoff M.. 2015. Formalising traffic rules for accountability of autonomous vehicles. In Proceedings of the IEEE 18th International Conference on Intelligent Transportation Systems (ITSC’15). IEEE, 16581665.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. [52] Rizaldi A., Keinholz J., Huber M., Feldle J., Immler F., Althoff M., Hilgendorf E., and Nipkow T.. 2017. Formalising and monitoring traffic rules for autonomous vehicles in Isabelle/HOL. In Proceedings of the 13th International Conference on Integrated Formal Methods (IFM’17), Lecture Notes in Computer Science, Vol. 10510, N. Polikarpova and S. A. Schneider (Eds.). Springer, 5066.Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Karimi A. and Duggirala P. S.. 2020. Formalizing traffic rules for uncontrolled intersections. In Proceedings of the 11th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS’20). IEEE, 4150.Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Zhou Z. Q. and Sun L.. 2019. Metamorphic testing of driverless cars. Commun. ACM 62, 3 (2019), 6167.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. [55] El-Hokayem Antoine, Bensalem Saddek, and Bozga Marius. 2020. Joseph Sifakis: A layered implementation of DR-BIP supporting run-time monitoring and analysis. In Proceedings of the International Conference on Software Engineering and Formal Methods (SEFM’20). 284302.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. [56] SAE International Releases Updated Visual Chart for Its “Levels of Driving Automation” Standard for Self-Driving Vehicles. Retrieved from https://www.sae.org/news/press-room/2018/12/sae-international-releases-updated-visual-chart-for-its-%E2%80%9Clevels-of-driving-automation%E2%80%9D-standard-for-self-driving-vehicles.Google ScholarGoogle Scholar
  57. [57] Jerrold M. and Grochow A.. 2020. Taxonomy of automated assistants. Commun. ACM 63 (2020), 3941.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. [58] Galdon Fernando, Hall Ashley, and Wang Stephen Jia. 2020. Designing trust in highly automated virtual assistants: A taxonomy of levels of autonomy. In Artificial Intelligence in Industry 4.0: A Collection of Innovative Research Case-studies.Google ScholarGoogle Scholar
  59. [59] Davis Ernest and Marcus Gary. 2015. Commonsense reasoning and commonsense knowledge in artificial intelligence. Commun. ACM 58, 9 (2015), 92103.Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. [60] NDTV Watch: Tesla Autopilot Feature Mistakes Moon For Yellow Traffic Light. Retrieved from July 27 2021 https://www.ndtv.com/offbeat/watch-tesla-autopilot-feature-mistakes-moon-for-yellow-trafficlight-2495804.Google ScholarGoogle Scholar
  61. [61] Neumann P. G.. 2017. Trustworthiness and truthfulness are essential. Commun. ACM 60, 6 (2017), 2628.Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. [62] Wikipedia. Precautionary principle. Retrieved from https://en.wikipedia.org/wiki/Precautionary_principle.Google ScholarGoogle Scholar
  63. [63] Dambrot Stuart Mason, Kerchove Derrick de, Flammini Francesco, Kinsner Witold, Glenn Linda MacDonald, and Saracco Roberto. 2018. IEEE Symbiotic Autonomous Systems White Paper ii.Google ScholarGoogle Scholar
  64. [64] Katz Guy, Barrett Clark, Dill David, Julian Kyle, and Kochenderfer Mykel. 2017. Reluplex: An efficient SMT solver for verifying deep neural networks. arXiv:1702.01135v2 [cs.AI]. Retrieved from https://arxiv.org/abs/1702.01135v2.Google ScholarGoogle Scholar
  65. [65] Franco Nicola, Wollschläger Tom, Gao Nicholas, Lorenz Jeanette Miriam, and Günnemann Stephan. 2022. Quantum robustness verification: A hybrid quantum-classical neural network certification algorithm. arXiv:2205.00900v1 [quant-ph]. Retrieved from https:arxiv.org/abs/2205.00900v1.Google ScholarGoogle Scholar

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