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A survey on verification strategies for intelligent transportation systems

Journal of Reliable Intelligent Environments volume 4, pages 211–224 (2018)Cite this article

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.

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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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Keywords

  • Autonomous vehicles
  • Intelligent transportation
  • Verification
  • Machine learning
  • Context