Generative Model-Based Simulation of Driver Behavior When Using Control Input Interface for Teleoperated Driving in Unstructured Canyon Terrains

Forward to the Future: A Deep Learning-based Approach to Mitigate Control Transmission Delay of Teleoperated Driving in Unstructured Environments

“…Evaluation Method: To assess the capability of our models to overcome transmission delays, we conducted a simulation model-based evaluation. Given that our three models-LSTM, DLinear, and TiDE-generate predicted control inputs for up to five time steps beyond the current time step, we evaluated driving performance under conditions featuring transmission delays ranging from a minimum of one time step to a maximum For our simulation-based evaluation, we employed a driving simulation model capable of generating control inputs of drivers in unstructured canyon terrains, as detailed in previous work [16]. This model is able to simulate the control inputs of drivers unfamiliar with unstructured canyon terrains in the game engine-based simulator.…”

“…Following the driving, we assessed the driving performance and computed the average performance across all four trials for comparison. The evaluation metrics included Standard Deviation of Lateral Position (SDLP) [14], average speed (AvgSpeed) [16], and average curvature of the path (Curvature) [20].…”