Generative Model-based Simulation of Driver Behavior when Using Control Input Interface for Teleoperated Driving in Unstructured Canyon Terrains
This work addresses the need for efficient simulation tools for teleoperated driving in unstructured terrains, offering a potential reduction in time and effort compared to traditional user studies, though it appears incremental as it builds on existing simulation and generative model approaches.
The paper tackles the problem of costly user studies for teleoperated driving in unstructured environments by proposing a generative model-based simulation of driver behavior, demonstrating that the model can appropriately generate simulated driver data in unstructured canyon terrains.
Unmanned ground vehicles (UGVs) in unstructured environments mostly operate through teleoperation. To enable stable teleoperated driving in unstructured environments, some research has suggested driver assistance and evaluation methods that involve user studies, which can be costly and require lots of time and effort. A simulation model-based approach has been proposed to complement the user study; however, the models on teleoperated driving do not account for unstructured environments. Our proposed solution involves simulation models of teleoperated driving for drivers that utilize a deep generative model. Initially, we build a teleoperated driving simulator to imitate unstructured environments based on previous research and collect driving data from drivers. Then, we design and implement the simulation models based on a conditional variational autoencoder (CVAE). Our evaluation results demonstrate that the proposed teleoperated driving model can generate data by simulating the driver appropriately in unstructured canyon terrains.