Towards Measuring the Performance of Architectural Components of Autonomous Vehicular Systems

Published

January 1, 2002

Author(s)

Lawrence A. Welsch, Rajmohan (. Madhavan, Craig I. Schlenoff

Abstract

For a vehicular system to act intelligent, the system must be able to 1) sense in a dynamic domain; 2) model the domain internally; 3)determine possible courses of action to accomplish a goal in the domain; and 4) be able to assess the various courses of actions to determine which is best. The actions that the system ultimately performs are a function of all of these components. Solely assigning performance metrics to the resultant action of the intelligent system does not evaluate any one of these components individually, and therefore leaves some doubt as to how to measure what each component contributes to the overall behavior of the system. Thus we are not looking at a single number, but a matrix of numbers that characterize the performance of the system. In this paper, we are exploring a mechanism to assign performance metrics to the part of the system that models the domain internally, the internal knowledge representation of intelligent vehicular systems. We do not consider that part of a system that translates the raw sensory input from a vehicle?s sensors to other representations. Rather we simulate a predefined set of sensory inputs, and evaluate the resulting knowledge representation based.

Proceedings Title

Proceedings of the Performing Metrics per Intelligent Systems (PerMIS) Workshop

Conference Dates

August 13-15, 2002

Conference Location

Gaithersburg, MD

Conference Title

Performing Metrics per Intelligent Systems (PerMIS) Workshop

Pub Type

Conferences

Keywords

autonomous vehicles, intelligent systems, IQ, RCS, test harness

Citation

Welsch, L. , Madhavan, R. and Schlenoff, C. (2002), Towards Measuring the Performance of Architectural Components of Autonomous Vehicular Systems, Proceedings of the Performing Metrics per Intelligent Systems (PerMIS) Workshop, Gaithersburg, MD (Accessed December 5, 2024)