  The rising presence of autonomous vehicles (AVs) on public roads necessitates
the development of advanced control strategies that account for the
unpredictable nature of human-driven vehicles (HVs). This study introduces a
learning-based method for modeling HV behavior, combining a traditional
first-principles approach with a Gaussian process (GP) learning component. This
hybrid model enhances the accuracy of velocity predictions and provides
measurable uncertainty estimates. We leverage this model to develop a GP-based
model predictive control (GP-MPC) strategy to improve safety in mixed vehicle
platoons by integrating uncertainty assessments into distance constraints.
Comparative simulations between our GP-MPC approach and a conventional model
predictive control (MPC) strategy reveal that the GP-MPC ensures safer
distancing and more efficient travel within the mixed platoon. By incorporating
sparse GP modeling for HVs and a dynamic GP prediction in MPC, we significantly
reduce the computation time of GP-MPC, making it only marginally longer than
standard MPC and approximately 100 times faster than previous models not
employing these techniques. Our findings underscore the effectiveness of
learning-based HV modeling in enhancing safety and efficiency in mixed-traffic
environments involving AV and HV interactions.
