Research Activities: 2012

Quantitative Modeling of Failure Propagation in Intelligent Transportation Systems

 Status Complete                  View Final Report: PDF
Sequential Number R346
Identification Number 00042531
Matching Research Agency

Missouri University of Science & Technology

Principal Investigator

Sahra Sedigh Sarvestani
Associate Professor
Missouri University of Science and Technology
Rolla, MO 65409
(573) 341-7505

Student Involvement

One graduate student


Project Objective
To develop a quantitative model that can capture and predict the occurrence and propagation of failure in networks of manned and unmanned vehicles.

Project Abstract
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.

Relationship to other Research/Projects

The investigators have established work in analysis of other critical infrastructure systems; namely, smart power grid and intelligent water distribution networks. The proposed research extends their work to the
transportation domain. They are also studying traffic control systems, in the context of pervasive computing.


Transportation-Related Keywords

Intelligent transportation systems, unmanned vehicles, driverless cars

Technology Transfer Activities

Results of the proposed work will be presented at the CIES/NUTC annual conference and other interdisciplinary conferences, and will be shared with automobile manufacturers (Ford), the California Departments of Transportation, and Google. All research results will be made available on the project and CIES web sites, respectively.

Project Deliverables

1. Qualitative model for an environment that includes both hybrid (manned and unmanned) vehicles
2. Limited quantitative model of the same environment
3. Simulation tool to be used for validation of quantitative models

Anticipated Benefits

Integration of intelligent devices with their legacy counterparts, and the hybrid environments that result, pose significant research and development challenges. To our knowledge, no existing work has rigorously studied this integration. The insights anticipated from the proposed investigation can be invaluable in refining the control algorithms of the intelligent devices, allowing them to operate in a safer and more efficient fashion.


Project Start Date: 05/01/2013
Project End Date: