  As autonomous vehicles (AVs) become more common on public roads, their
interaction with human-driven vehicles (HVs) in mixed traffic is inevitable.
This requires new control strategies for AVs to handle the unpredictable nature
of HVs. This study focused on safe control in mixed-vehicle platoons consisting
of both AVs and HVs, particularly during longitudinal car-following scenarios.
We introduce a novel model that combines a conventional first-principles model
with a Gaussian process (GP) machine learning-based model to better predict HV
behavior. Our results showed a significant improvement in predicting HV speed,
with a 35.64% reduction in the root mean square error compared with the use of
the first-principles model alone. We developed a new control strategy called
GP-MPC, which uses the proposed HV model for safer distance management between
vehicles in the mixed platoon. The GP-MPC strategy effectively utilizes the
capacity of the GP model to assess uncertainties, thereby significantly
enhancing safety in challenging traffic scenarios, such as emergency braking
scenarios. In simulations, the GP-MPC strategy outperformed the baseline MPC
method, offering better safety and more efficient vehicle movement in mixed
traffic.
