A Decision Analytic Approach to Incorporating Value of Information in Autonomous Systems

Selecting the right transportation platform is challenging, whether it is at a personal level or at an organizational level. In settings where predominantly the functional aspects rule the decision making process, defining the mobility of a vehicle is critical for comparing different offerings and making acquisition decisions. With the advent of intelligent vehicles, exhibiting partial to full autonomy, this challenge is exacerbated. The same vehicle may traverse independently and with greater tolerance for acceleration than human occupied vehicles, while, at the same time struggle with obstacle avoidance. The problem presents itself at the individual vehicle sensing level and also at the vehicle/fleet level. At the sensing and information level, one can be looking at issues of latency, bandwidth and optimal information fusion from multiple sources including privileged sensing. At the overall vehicle level, one focuses more on the ability to complete missions. Clearly, decisions at the sensing level impact vehicle level decisions and a common paradigm for both is not only possible, but also desirable. In this paper, we use a decision theoretic model of mobility and describe how mobility can be defined for intelligent vehicles exhibiting some form of autonomy. The decision theoretic method is then applied on the simulation of three vehicle types on a chosen path with the help of a simulation software. The results show that multiattribute utility analysis is a viable model for decision making for vehicle selection as well as calculation of the value of information in situations where performance uncertainty of the vehicles is present. Index Terms Mobility, Decision Theory.


  • English

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  • Media Type: Digital/other
  • Features: Figures; References; Tables;
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Filing Info

  • Accession Number: 01726343
  • Record Type: Publication
  • Source Agency: SAE International
  • Report/Paper Numbers: 2018-01-0799
  • Files: TRIS, SAE
  • Created Date: Dec 20 2019 4:26PM