Quantitative Modeling of Failure Propagation in Intelligent Transportation Systems

Unmanned vehicles are projected to reach consumer use within this decade - related legislation has already passed in California. The most significant technical challenge associated with these vehicles is their integration in transportation environments with manned vehicles. Abnormal or incorrect manipulation of the manned vehicles by their human drivers creates a highly nondeterministic environment that is difficult to consider in the control algorithms for unmanned vehicles. Our ultimate goal is to develop a Markovian model that can capture the stochastic elements of this environment, in particular failure propagation from the manned to unmanned vehicles and vice versa. The analytic model will be validated through simulation with a purpose built tool that we plan to develop in the course of the proposed work. In the nine months of the project, we expect to create a qualitative model for the environment, to begin work on the quantitative model (using Petri nets and the qualitative model as a basis), and to develop the simulation environment required.

Language

  • English

Project

  • Status: Completed
  • Funding: $16052.00
  • Contract Numbers:

    DTRT06-G-0014

    00042531

  • Sponsor Organizations:

    Research and Innovative Technology Administration

    University Transportation Centers Program
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590

    Missouri University of Science and Technology, Rolla

    328 Butler-Carlton Hall
    1401 N. Pine Street
    Rolla, MO  United States  65401
  • Principal Investigators:

    Sedigh Sarvestani, Sahra

  • Start Date: 20130501
  • Expected Completion Date: 0
  • Actual Completion Date: 20131231
  • Source Data: RiP Project 34932

Subject/Index Terms

Filing Info

  • Accession Number: 01534472
  • Record Type: Research project
  • Source Agency: Center for Infrastructure Engineering Studies
  • Contract Numbers: DTRT06-G-0014, 00042531
  • Files: UTC, RiP
  • Created Date: Aug 14 2014 1:00AM