Modeling Human Steering Behavior During Path Following in Teleoperation of Unmanned Ground Vehicles

Citation metadata

Date: Aug. 2018
From: Human Factors(Vol. 60, Issue 5)
Publisher: Sage Publications, Inc.
Document Type: Article
Length: 263 words

Document controls

Main content

Abstract :

Objective: This paper presents a behavioral model representing the human steering performance in teleoperated unmanned ground vehicles (UGVs). Background: Human steering performance in teleoperation is considerably different from the performance in regular onboard driving situations due to significant communication delays in teleoperation systems and limited information human teleoperators receive from the vehicle sensory system. Mathematical models capturing the teleoperation performance are a key to making the development and evaluation of teleoperated UGV technologies fully simulation based and thus more rapid and cost-effective. However, driver models developed for the typical onboard driving case do not readily address this need. Method: To fill the gap, this paper adopts a cognitive model that was originally developed for a typical highway driving scenario and develops a tuning strategy that adjusts the model parameters in the absence of human data to reflect the effect of various latencies and UGV speeds on driver performance in a teleoperated path-following task. Results: Based on data collected from a human subject test study, it is shown that the tuned model can predict both the trend of changes in driver performance for different driving conditions and the best steering performance of human subjects in all driving conditions considered. Conclusions: The proposed model with the tuning strategy has a satisfactory performance in predicting human steering behavior in the task of teleoperated path following of UGVs. Application: The established model is a suited candidate to be used in place of human drivers for simulation-based studies of UGV mobility in teleoperation systems. Keywords: human performance modeling, teleoperation, driver behavior, ACT-R cognitive architecture, computational modeling DOI: 10.1177/0018720818769260

Source Citation

Source Citation   

Gale Document Number: GALE|A547869229