Learning Teleoperated Driving Behavior from Limited Trajectory Data

In this paper, we propose models with explicit trainable delays for learning teleoperated driving (ToD) behavior from limited vehicle trajectory data. The data-driven model is integrated with physics-based nonlinear vehicle dynamics and formulated as a neural delay differential equation (NDDE). The model can be analyzed using the same tools as developed for classical delay differential equations. The physics-based nonlinearity built into the data-driven model reduces the model complexity, enables training with limited data, and provides good generalizations. An overall latency in the loop is learned and a generic steering controller that characterizes the remote operator is identified at the same time through the training process. This information could be used to evaluate the performance of ToD in the presence of communication latency. We provide examples of learning from simulation data generated by a kinematic vehicle model and from experimental data generated by a human operator driving in a high-fidelity simulation environment. The same data-driven model and training algorithm is used in both cases, which demonstrates the generalizability of the proposed approach.