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A survey on verification strategies for intelligent transportation systems

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Abstract

As intelligent systems are increasingly entering everyday life, in domains such as transportation, resource distribution, health care, or retail, developing suitable verification mechanisms for such systems becomes vital. From a formal point of view, the employed intelligent sensor actuator systems (ISAS) constituting such intelligent systems combine three different technologies: control systems, distributed systems, and learning and reasoning. While each of the parent domains features tested and proven verification methods, simply combining the tasks unfortunately leads to a combinatorial explosion of complexity. This paper presents an overview and classification of currently employed techniques for handling ISAS in terms of: cyber-physical systems, intelligent autonomous robots, or intelligent agents. The article argues that each of the three classical perspectives misses one important characteristic of ISAS and proposes to combine the three for a full solution. The paper argues that in particular two mechanisms are promising: an intelligent environments perspective that verifies local safety and techniques for context-aware monitoring that allow a mobile system to leverage context-awareness to reduce complexity for self-monitoring tasks.

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Notes

  1. http://humanrobotinteraction.org

  2. This is one of the reasons why drivers of autonomous cars fail to prevent accidents: it takes considerable time for a human being to understand a complex situation, so as to filter and select among the wealth of available possible actions an appropriate one. While an alert driver handling an ongoing incrementally changing driving context, is at any time within a properly filtered context and able to react within a one second delay, a driver relying on self-driving capabilities of an autonomous car, will require a considerably extended comprehension period for acquiring the specific driving context to be added to reaction time. With respect to the literature on driver’s reaction times, this case corresponds to one of reduced visibility [84], known to increase reaction times.

References

  1. Baheti R, Gill H (2011) Cyber-physical systems. Impact Control Technol 12:161–166

    Google Scholar 

  2. Behnke S, Sheh R, Sarıel S, Lee DD (2017) RoboCup 2016: Robot World Cup XX. Springer

  3. Bettini C, Brdiczka O, Henricksen K, Indulska J, Nicklas D, Ranganathan A, Riboni D (2010) A survey of context modelling and reasoning techniques. Pervas Mobile Comput 6(2):161–180

    Article  Google Scholar 

  4. Boole G (1854) An investigation of the laws of thought: on which are founded the mathematical theories of logic and probabilities. Dover Publications, New York

    MATH  Google Scholar 

  5. Boytsov A, Zaslavsky A (2013) Formal verification of context and situation models in pervasive computing. Pervas Mobile Comput 9(1):98–117

    Article  Google Scholar 

  6. Branicky MS, Borkar VS, Mitter SK (1998) A unified framework for hybrid control: model and optimal control theory. IEEE Trans Autom Control 43(1):31–45

    Article  MathSciNet  Google Scholar 

  7. Brogan WL (1990) Modern control theory. Pearson,

  8. Cardelli L, Gordon AD (2000) Mobile ambients. Theoret Comput Sci 240(1):177–213

    Article  MathSciNet  Google Scholar 

  9. Clarke E, Emerson E (1982) Design and synthesis of synchronization skeletons using branching time temporal logic. Logics Program 52–71

  10. Cohn AG, Hazarika SM (2001) Qualitative spatial representation and reasoning: an overview. Fundamenta Informaticae 46(1–2):1–29

    MathSciNet  MATH  Google Scholar 

  11. Corcoran J (1973) A mathematical model of Aristotle’s syllogistic. Archiv für Geschichte der Philosophie 55(2):191–219

    Article  MathSciNet  Google Scholar 

  12. Coronato A, Pietro GD (2012) Tools for the rapid prototyping of provably correct ambient intelligence applications. IEEE Trans Softw Eng 38(4):975–991

    Article  Google Scholar 

  13. Dey AK, Abowd GD (2000) Towards a better understanding of context and context-awareness. In: Workshop on the what, who, where, when, and how of context-awareness. ACM

  14. Egenhofer MJ (1994) Spatial SQL: a query and presentation language. IEEE Trans Knowl Data Eng 6(1):86–95

    Article  Google Scholar 

  15. Egenhofer MJ, Mark DM (1995) Naive geography. In: Frank AU, Kuhn W (eds) Information Spatial Theory, A Theoretical Basis for GIS. Springer, pp 1–15

  16. European Union (2016) General data protection regulation. http://data.europa.eu/eli/reg/2016/679/oj. Accessed 4 Oct 2018

  17. Fagin R, Halpern JY, Moses Y, Vardi M (2004) Reasoning about knowledge. MIT press, USA

    MATH  Google Scholar 

  18. Floyd RW (1967) Assigning meanings to programs. Program Verif 14:65–81

  19. Forbus KD (1984) Qualitative process theory. Artif Intell 24(1):85–168

  20. Frege G (1879) Begriffsschrift, eine der arithmetischen nachgebildete Formelsprache des reinen Denkens. L. Nebert

  21. Freksa C (1991) Qualitative spatial reasoning. In: Cognitive and linguistic aspects of geographic space. Springer, New York, pp 361–372

    Chapter  Google Scholar 

  22. Freksa C (1992) Temporal reasoning based on semi-intervals. Artif Intell 54(1–2):199–227

    Article  MathSciNet  Google Scholar 

  23. Gajski DD, Vahid F, Narayan S, Gong J (1994) Specification and design of embedded systems, vol 13. Prentice Hall, Englewood Cliffs

  24. Galton A (2000) Qualitative spatial change. Oxford University Press, Oxford

    MATH  Google Scholar 

  25. Gärdenfors P (2005) The detachment of thought. In: Erneling C, Johnson D (eds) The mind as a scientific subject: between brain and culture. Oxford University Press, Oxford, pp 323–341

  26. Guarino N (1998) Formal ontology and information systems. In: Guarino N (ed) Formal Ontol Inf Syst. IOS Press, Amsterdam, pp 3–15

    Google Scholar 

  27. Haarslev V, Lutz C, Möller R (1999) A description logic with concrete domains and a role-forming predicate operator. J Logic Comput 9(3):351–384

    Article  MathSciNet  Google Scholar 

  28. Harnad S (1990) The symbol grounding problem. Phys D Nonlinear Phenom 42(1–3):335–346

    Article  Google Scholar 

  29. Havelund K, Shankar N (1996) Experiments in theorem proving and model checking for protocol verification. In: International symposium of formal methods Europe. Springer, pp 662–681

  30. Hawblitzel C, Howell J, Kapritsos M, Lorch JR, Parno B, Roberts ML, Setty S, Zill B (2015) Ironfleet: proving practical distributed systems correct. In: Proceedings of the 25th symposium on operating systems principles. ACM, pp 1–17

  31. Hayes P (1985) The second naive physics manifesto. In: Hobbs J, Moore R (eds) Theories of the commonsense world. Ablex Publishing Corporation, Norwood, pp 1–36

    Google Scholar 

  32. Hayes PJ et al (1978) The naive physics manifesto. Tech. rep., Université de Genève, Institut pour les études sémantiques et cognitives

  33. Hennessy M (2007) A distributed pi-calculus. Cambridge University Press, Cambridge

    Book  Google Scholar 

  34. Henricksen K, Indulska J (2006) Developing context-aware pervasive computing applications: models and approach. Pervas Mobile Comput 2:37–64

    Article  Google Scholar 

  35. Hoare CAR, Jifeng H (1998) Unifying theories of programming, vol 14. Prentice Hall, Englewood Cliffs

  36. Holzmann GJ (1990) Algorithms for automated protocol verification. AT&T Techn J 69(1):32–44

    Article  Google Scholar 

  37. Hupfeld F, Beigl M (2000) Spatially aware local communication in the RAUM system. In: IDMS. Springer, pp 285–296

  38. Jang S, Woo W (2003) ubi-UCAM: a unified context-aware application model. In: Blackburn P, Ghidini C, Turner RM, Giunchiglia F (eds) International conference on modeling and using context, pp 178–189

  39. Jiang C, Steenkiste P (2002) A hybrid location model with a computable location identifier for ubiquitous computing. In: Borriello G, Holmquist LE (eds) Ubiquitous Comput. Springer, Gothenburg, pp 246–263

    MATH  Google Scholar 

  40. Kamali M, Dennis LA, McAree O, Fisher M, Veres SM (2017) Formal verification of autonomous vehicle platooning. Sci Comput Program 148:88–106

    Article  Google Scholar 

  41. Kawahara R, Dotan D, Sakairi T, Ono K, Nakamura H, Kirshin A, Hirose S, Ishikawa H (2009) Verification of embedded system’s specification using collaborative simulation of sysml and simulink models. In: Model-based systems engineering, 2009. MBSE’09. International Conference on, IEEE, pp 21–28

  42. Khaitan SK, McCalley JD (2015) Design techniques and applications of cyberphysical systems: a survey. IEEE Syst J 9(2):350–365

    Article  Google Scholar 

  43. Kitchin R (2014) The real-time city? big data and smart urbanism. GeoJ 79(1):1–14

    Article  Google Scholar 

  44. Kloetzer M, Belta C (2010) Automatic deployment of distributed teams of robots from temporal logic motion specifications. IEEE Trans Robot 26(1):48–61

    Article  Google Scholar 

  45. Kress-Gazit H, Fainekos GE, Pappas GJ (2009) Temporal-logic-based reactive mission and motion planning. IEEE Trans Robot 25(6):1370–1381

    Article  Google Scholar 

  46. Kuipers B (2000) The spatial semantic hierarchy. Artif Intell 119(1–2):191–233

    Article  MathSciNet  Google Scholar 

  47. Kumar P, Goswami D, Chakraborty S, Annaswamy A, Lampka K, Thiele L (2012) A hybrid approach to cyber-physical systems verification. In: Proceedings of the 49th annual design automation conference. ACM, pp 688–696

  48. Lamport L (1994) The temporal logic of actions. ACM Trans Program Lang Syst (TOPLAS) 16(3):872–923

    Article  Google Scholar 

  49. Langheinrich M (2001) Privacy by design—principles of privacy-aware ubiquitous systems. In: Abowd GD, Brumitt B, Shafer S (eds) Ubiquitous computing. Springer, Heidelberg, pp 273–291

    MATH  Google Scholar 

  50. Lee EA (2008) Cyber physical systems: Design challenges. In: Object oriented real-time distributed computing (ISORC), 2008 11th IEEE international symposium on IEEE, pp 363–369

  51. Lekshmy VG, Bhaskar J (2015) Programming smart environments using \(\pi \)-calculus. Procedia Comput Sci 46:884–891

    Article  Google Scholar 

  52. Lenzen W (2004) Calculus Universalis. Studien zur Logik von GW Leibniz, Mentis, Paderborn

  53. Leucker M, Schallhart C (2009) A brief account of runtime verification. J Logic Algebraic Program 78:293–303

    Article  Google Scholar 

  54. Levesque HJ, Brachman RJ (1987) Expressiveness and tractability in knowledge representation and reasoning. Comput Intel 3(2):78–93

    Article  Google Scholar 

  55. Lin FJ, Chu P, Liu MT (1987) Protocol verification using reachability analysis: the state space explosion problem and relief strategies. ACM SIGCOMM Comput Commun Rev 17(5):126–135

    Article  Google Scholar 

  56. Liu HY (2017) Irresponsibilities, inequalities and injustice for autonomous vehicles. Ethics Inf Technol 19(3):193–207. https://doi.org/10.1007/s10676-017-9436-2

    Article  Google Scholar 

  57. Lomuscio A, Sergot M (2003) Deontic interpreted systems. Studia Logica 75(1):63–92

    Article  MathSciNet  Google Scholar 

  58. Lomuscio A, Qu H, Raimondi F (2009) Mcmas: A model checker for the verification of multi-agent systems. In: International conference on computer aided verification. Springer, pp 682–688

  59. Lyons DM, Arkin RC, Jiang S, Liu TM, Nirmal P (2015) Performance verification for behavior-based robot missions. IEEE Trans Robot 31(3):619–636

    Article  Google Scholar 

  60. del Mar Gallardo M, Lavado L, Panizo L, Titolo L (2017) A constraint-based language for modelling intelligent environments. J Reliab Intell Environ 3(1):55–79

    Article  Google Scholar 

  61. Merola L (2006) The COTS software obsolescence threat. In: Fifth international conference on commercial-off-the-Shelf (COTS)-based software systems (ICCBSS’05), pp 127–133. https://doi.org/10.1109/ICCBSS.2006.29

  62. Milner R (2006a) Pervasive process calculus. Electron Notes Theoret Comput Sci 162:255–259

    Article  Google Scholar 

  63. Milner R (2006b) Ubiquitous computing: shall we understand it? Comput J 49(4):383–389

    Article  Google Scholar 

  64. Milner R (2008) Bigraphs and their algebra. Electron Notes Theoret Comput Sci 209:5–19

    Article  MathSciNet  Google Scholar 

  65. Nagel E, Newman JR, Hofstadter DR (2001) Gödel’s proof. New York University Press, New York

    Google Scholar 

  66. Nardi D, Brachman RJ (2002) An introduction to description logics. In: McGuinness D, Nardi D, Patel-Schneider P (eds) F Baader DC. Description Logic Handbook. Cambridge University Press, Cambridge, pp 5–44

  67. National Transportation Safety Board (2017) Collision between a car operating with automated vehicle control systems and a tractor-semitrailer truck near williston, florida may 7, (2016) Highway Accident Report NTSB/HAR-17/02. National Transportation Safety Board, Washington, DC

  68. Nisan N, Roughgarden T, Tardos E, Vazirani VV (2007) Algorithmic game theory, vol 1. Cambridge University Press, Cambridge

    Book  Google Scholar 

  69. Passino KM, Yurkovich S, Reinfrank M (1998) Fuzzy control. Addison-Wesley, USA

    Google Scholar 

  70. Prior A (1968) now. Nous 2:101–119

  71. Rajkumar RR, Lee I, Sha L, Stankovic J (2010) Cyber-physical systems: the next computing revolution. In: Proceedings of the 47th design automation conference. ACM, pp 731–736

  72. Randell D, Cui Z, Cohn A (1992) A spatial logic based on region and connection. In: Knowledge representation and reasoning. Morgan Kaufmann, pp 165–176

  73. Ranganathan A, Campbell RH (2008) Provably correct pervasive computing environments. In: PerCom, pp 160–169

  74. Schmidtke HR (2016) Granular mereogeometry. In: Ferrario R, Kuhn W (eds) Formal ontology in information systems. In: Proceedings of the 9th international conference (FOIS 2016), IOS Press, Frontiers in Artificial Intelligence and Applications, vol 283, pp 81–94

  75. Schmidtke HR (2018) Logical lateration—a cognitive systems experiment towards a new approach to the grounding problem. Cognit Syst Res. https://doi.org/10.1016/j.cogsys.2018.09.008

    Article  Google Scholar 

  76. Schmidtke HR, Beigl M (2011) Distributed spatial reasoning for wireless sensor networks. In: Modeling and using context. Springer, pp 264–277

  77. Schmidtke HR, Woo W (2007) A size-based qualitative approach to the representation of spatial granularity. In: Veloso MM (ed) Twentieth international joint conference on artificial intelligence, pp 563–568

  78. Schmidtke HR, Woo W (2008) Partial ordering constraints for representations of context in ambient intelligence applications. In: Villadsen J, Christiansen H (eds) Constraints and language processing, pp 61–75

  79. Schmidtke HR, Woo W (2009) Towards ontology-based formal verification methods for context aware systems. In: Tokuda H, Beigl M, Brush A, Friday A, Tobe Y (eds) Pervasive 2009. Springer, pp 309–326

  80. Schmidtke HR, Hong D, Woo W (2008) Reasoning about models of context: A context-oriented logical language for knowledge-based context-aware applications. Revue d’Intelligence Artificielle 22(5):589–608

    Article  Google Scholar 

  81. Sheridan TB (2016) Human-robot interaction: status and challenges. Hum Factors 58(4):525–532

    Article  Google Scholar 

  82. Singh MP (1999) An ontology for commitments in multiagent systems. Artif Intell Law 7(1):97–113

    Article  Google Scholar 

  83. Srzednicki JJ, Stachniak Z (eds) (2012) Leśniewski’s Systems Protothetic, Nijhoff International Philosophy Series, vol 54. Springer, Netherlands

  84. Stanisław Jurecki R, Lech Stańczyk T, Jacek Jaśkiewicz M (2017) Driver’s reaction time in a simulated, complex road incident. Transport 32(1):44–54

    Article  Google Scholar 

  85. Steels L (2008) The symbol grounding problem has been solved. so what’s next. Symbols and embodiment: Debates on meaning and cognition pp 223–244

    Chapter  Google Scholar 

  86. Steinfeld A, Fong T, Kaber D, Lewis M, Scholtz J, Schultz A, Goodrich M (2006) Common metrics for human-robot interaction. In: Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction. ACM, pp 33–40

  87. Tarski A (1956) Foundations of the geometry of solids. In: Logic, Semantics, Metamathematics. Papers from 1923 to 1938. Clarendon, Oxford, pp 24–29

  88. UN General Assembly (1948) Universal declaration of human rights http://www.un.org/en/universal-declaration-human-rights/. Accessed 16 Apr 2018

  89. Vogt P (2002) The physical symbol grounding problem. Cognit Syst Res 3(3):429–457

    Article  MathSciNet  Google Scholar 

  90. Wachter S, Mittelstadt B, Floridi L (2017) Transparent, explainable, and accountable ai for robotics. Sci Robot 2(6)

    Article  Google Scholar 

  91. Walsh GC, Ye H, Bushnell LG (2002) Stability analysis of networked control systems. IEEE Trans Control Syst Technol 10(3):438–446

    Article  Google Scholar 

  92. Waytz A, Epley N, Cacioppo JT (2010) Social cognition unbound: Insights into anthropomorphism and dehumanization. Curr Direct Psychol Sci 19(1):58–62

    Article  Google Scholar 

  93. Waytz A, Heafner J, Epley N (2014) The mind in the machine: anthropomorphism increases trust in an autonomous vehicle. J Exp Soc Psychol 52:113–117

    Article  Google Scholar 

  94. Weis T, Becker C, Brändle A (2006) Towards a programming paradigm for pervasive applications based on the ambient calculus. In: Workshop on combining theory and systems building in pervasive computing

  95. Wessel M (2001) Obstacles on the way to qualitative spatial reasoning with description logics: some undecidability results. Descrip Logics 49

  96. Whitehead AN, Russell B (1912) Principia mathematica. University Press,

  97. Winfield AF, Nembrini J (2006) Safety in numbers: fault-tolerance in robot swarms. Int J Modell Identif Control 1(1):30–37

    Article  Google Scholar 

  98. Wooldridge M (1997) Agent-based software engineering. IEE Proc Softw 144(1):26–37

    Article  Google Scholar 

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    Hedda R. Schmidtke

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Schmidtke, H.R. A survey on verification strategies for intelligent transportation systems. J Reliable Intell Environ 4, 211–224 (2018). https://doi.org/10.1007/s40860-018-0070-5

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