This paper presents a steering model for predicting human performance in teleoperating unmanned ground vehicles (UGVs). The task of path following, including lane keeping and curve negotiation, is considered for a UGV teleoperation system. Human steering performance in teleoperation is notably different than steering performance in on-board driving conditions due to considerable communication delays in remote teleoperation systems and limited information teleoperators receive from the vehicle sensory system. This paper adopts a cognitive model that was originally developed for a typical highway driving scenario when driver is on board and develops a tuning strategy to adjust the model parameters without human data to reflect the effect of various latencies and UGV speeds on driver performance in a teleoperated path following task. It is shown that the proposed model with tuning strategy i) can adequately capture the trend of changes in driver performance for different teleoperated driving scenarios ii) is able to predict an expert human teleoperator’s performance across different speeds and latencies considered. Thus, the tuned model can be an appropriate candidate to be used in place of human drivers for the simulation-based evaluation of UGV mobility in teleoperation systems.
- Dynamic Systems and Control Division
A Driver Model for Predicting Human Steering Performance in Teleoperated Path Following of Unmanned Ground Vehicles
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Mirinejad, H, Jayakumar, P, & Ersal, T. "A Driver Model for Predicting Human Steering Performance in Teleoperated Path Following of Unmanned Ground Vehicles." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications. Tysons, Virginia, USA. October 11–13, 2017. V002T21A001. ASME. https://doi.org/10.1115/DSCC2017-5086
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