Reconciling Simultaneous Evolution of Ground Vehicle Capabilities and Operator Preferences

Abstract

An objective evaluation of ground vehicle performance is a challenging task. This is further exacerbated by the increasing level of autonomy, dynamically changing the roles and capabilities of these vehicles. In the context of decision making involving these vehicles, as the capabilities of the vehicles improve, there is a concurrent change in the preferences of the decision makers operating the vehicles that must be accounted for. Decision based methods are a natural choice when multiple conflicting attributes are present, however, most of the literature focuses on static preferences. In this paper, we provide a sequential Bayesian framework to accommodate time varying preferences. The utility function is considered a stochastic function with the shape parameters themselves being random variables. In the proposed approach, initially the shape parameters model either uncertain preferences or variation in the preferences because of the presence of multiple decision makers. We consider this utility distribution as the prior and update it to a posterior with feedback that can be acquired from actual system use. The framework improves the utility function and thereby the decisions made for the next generation systems, allowing continuous improvement. We present our approach on a ground vehicle selection problem.

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Document Details

Document Type
Technical Report
Publication Date
Nov 08, 2019
Accession Number
AD1093491

Entities

People

  • Christopher Slon
  • David Gorsich
  • Paramsothy Jayakumar
  • Vijitashwa Pandey

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Acquisition
  • Autonomous Vehicles
  • Autonomy
  • Bayesian Networks
  • Computational Science
  • Department Of Defense
  • Engineering
  • Ground Vehicles
  • Literature
  • Mathematical Models
  • Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Analysis
  • Vehicles

Readers

  • Statistical inference.
  • Systems Analysis and Design
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference