Modeling Human Steering Behavior in Teleoperation of Unmanned Ground Vehicles With Varying Speed

Objective This paper extends a prior human operator model to capture human steering performance in the teleoperation of unmanned ground vehicles (UGVs) in path-following scenarios with varying speed. Background A prior study presented a human operator model to predict human steering performance in the teleoperation of a passenger-sized UGV at constant speeds. To enable applications to varying speed scenarios, the model needs to be extended to incorporate speed control and be able to predict human performance under the effect of accelerations/decelerations and various time delays induced by the teleoperation setting. A strategy is also needed to parameterize the model without human subject data for a truly predictive capability. Method This paper adopts the ACT-R cognitive architecture and two-point steering model used in the previous work, and extends the model by incorporating a far-point speed control model to allow for varying speed. A parameterization strategy is proposed to find a robust set of parameters for each time delay to maximize steering performance. Human subject experiments are conducted to validate the model. Results Results show that the parameterized model can predict both the trend of average lane keeping error and its lowest value for human subjects under different time delays. Conclusions The proposed model successfully extends the prior computational model to predict human steering behavior in a teleoperated UGV with varying speed. Application This computational model can be used to substitute for human operators in the process of development and testing of teleoperated UGV technologies and allows fully simulation-based development and studies.

Automated summary

This paper extends a prior human operator model to predict human steering performance in path-following scenarios of unmanned ground vehicles (UGVs) with varying speed. The proposed model successfully captures human steering behavior under the effect of accelerations/decelerations, time delays, and varying speed. Human subject experiments validate the model's performance.