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Performance Evaluation of Cost-Based vs. Fuzzy-Logic-Based Prediction Approaches in PRIDE

Published

Author(s)

Zeid Kootbally, Craig I. Schlenoff, Rajmohan Madhavan, Sebti Foufou

Abstract

PRIDE (PRediction In Dynamic Environments) is a hierarchical multi-resolutional framework for moving object prediction. PRIDE incorporates multiple prediction algorithms into a single, unifying framework. To date, we have applied this framework to predict the future location of autonomous vehicles during on-road driving. In this paper, we describe two different approaches to compute long-term predictions (on the order of seconds into the future) within PRIDE. The first is a cost-based approach that uses a discretized set of vehicle motions and costs associated with states and actions to compute probabilities of vehicle motion. The cost-based approach is the first prediction approach we have been using within PRIDE. The second is a fuzzy-logic-based approach that deals with the pervasive presence of uncertainty in the environment to negotiate complex traffic situations. Using the high-fidelity physics-based framework for the Unified System for Automation and Robot Simulation (USARSim), we compare the performance of the two approaches in different driving situations at traffic intersections. Consequently, we show how the two approaches complement each other and how their combination performs better than the cost-based approach only.
Proceedings Title
SPIE Defense and Security Symposium 2008
Conference Dates
March 16-20, 2008
Conference Location
Orlando, FL

Keywords

4D/RCS, autonomous vehicles, cost-based approach, fuzzy-logic-based approach, fuzzy sets, fuzzy control, moving object prediction, PRIDE

Citation

Kootbally, Z. , Schlenoff, C. , Madhavan, R. and Foufou, S. (2008), Performance Evaluation of Cost-Based vs. Fuzzy-Logic-Based Prediction Approaches in PRIDE, SPIE Defense and Security Symposium 2008, Orlando, FL, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824651 (Accessed April 23, 2024)
Created March 20, 2008, Updated February 19, 2017