Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient
  Control of Autonomous and Human-Driven Vehicles
Jie Wang Yash Vardhan Pant Lei Zhao Michał Antkiewicz Krzysztof Czarnecki
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.