  With the increasing presence of autonomous vehicles (AVs) on public roads,
developing robust control strategies to navigate the uncertainty of
human-driven vehicles (HVs) is crucial. This paper introduces an advanced
method for modeling HV behavior, combining a first-principles model with
Gaussian process (GP) learning to enhance velocity prediction accuracy and
provide a measurable uncertainty. We validated this innovative HV model using
real-world data from field experiments and applied it to develop a GP-enhanced
model predictive control (GP-MPC) strategy. This strategy aims to improve
safety in mixed vehicle platoons by integrating uncertainty assessment into
distance constraints. Comparative simulation studies with a conventional model
predictive control (MPC) approach demonstrated that our GP-MPC strategy ensures
more reliable safe distancing and fosters efficient vehicular dynamics,
achieving notably higher speeds within the platoon. By incorporating a sparse
GP technique in HV modeling and adopting a dynamic GP prediction within the MPC
framework, we significantly reduced the computation time of GP-MPC, marking it
only 4.6% higher than that of the conventional MPC. This represents a
substantial improvement, making the process about 100 times faster than our
preliminary work without these approximations. Our findings underscore the
effectiveness of learning-based HV modeling in enhancing both safety and
operational efficiency in mixed-traffic environments, paving the way for more
harmonious AV-HV interactions.
