Learning-Based Modeling of Human-Autonomous Vehicle Interaction for Enhancing Safety in Mixed-Vehicle Platooning Control

As autonomous vehicles (AVs) become more prevalent on public roads, they will inevitably interact with human-driven vehicles (HVs) in mixed traffic scenarios. To ensure safe interactions between AVs and HVs, it is crucial to account for the uncertain behaviors of HVs when developing control strategies for AVs. In this paper, we propose an efficient learning-based modeling approach for HVs that combines a first-principles model with a Gaussian process (GP) learning-based component. The GP model corrects the velocity prediction of the first-principles model and estimates its uncertainty. Utilizing this model, a model predictive control (MPC) strategy, referred to as GP-MPC, was designed to enhance the safe control of a mixed vehicle platoon by integrating the uncertainty assessment into the distance constraint. We compare our GP-MPC strategy with a baseline MPC that uses only the first-principles model in simulation studies. We show that our GP-MPC strategy provides more robust safe distance guarantees and enables more efficient travel behaviors (higher travel speeds) for all vehicles in the mixed platoon. Moreover, by incorporating a sparse GP technique in HV modeling and a dynamic GP prediction in MPC, we achieve an average computation time for GP-MPC at each time step that is only 5% longer than the baseline MPC, which is approximately 100 times faster than our previous work that did not use these approximations. This work demonstrates how learning-based modeling of HVs can enhance safety and efficiency in mixed traffic involving AV-HV interaction.