Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Ontology-Based Methods for Enhancing Autonomous Vehicle Path Planning



R Provine, Craig I. Schlenoff, Stephen B. Balakirsky, S Smith, M Uschold


We report the results of a first implementation demonstrating the use of an ontology to support reasoning about obstacles to improve the capabilities and performance of on-board route planning for autonomous vehicles. This is part of an overall effort to evaluate the performance of ontologies in different components of an autonomous vehicle within the 4D/RCS system architecture developed at NIST. Our initial focus has been on simple roadway driving scenarios where the controlled vehicle encounters potential obstacles in its path. As reported elsewhere [9], our approach is to develop an ontology of objects in the environment, in conjunction with rules for estimating the damage that would be incurred by collisions with different objects in different situations. Automated reasoning is used to estimate collision damage; this information is fed to the route planner to help it decide whether to plan to avoid the object. We describe the results of the first implementation that integrates the ontology, the reasoner and the planner. We describe our insights and lessons learned and discuss resulting changes to our approach.
Proceedings Title
Journal of Robotics and Autonomous Systems
Conference Dates
November 1, 2004
Conference Location


autonomous vehicle, Knowledge Engineering, Mobility, ontologies, path planning, qualitative reasoning, Robotics & Intelligent Systems


Provine, R. , Schlenoff, C. , Balakirsky, S. , Smith, S. and Uschold, M. (2004), Ontology-Based Methods for Enhancing Autonomous Vehicle Path Planning, Journal of Robotics and Autonomous Systems, , USA, [online], (Accessed April 15, 2024)
Created October 31, 2004, Updated October 12, 2021