  Human-leading truck platooning systems have been proposed to leverage the
benefits of both human supervision and vehicle autonomy. Equipped with human
guidance and autonomous technology, human-leading truck platooning systems are
more versatile to handle uncertain traffic conditions than fully automated
platooning systems. This paper presents a novel distributed stochastic model
predictive control (DSMPC) design for a human-leading heavy-duty truck platoon.
The proposed DSMPC design integrates the stochastic driver behavior model of
the human-driven leader truck with a distributed formation control design for
the following automated trucks in the platoon. The driver behavior of the
human-driven leader truck is learned by a stochastic inverse reinforcement
learning (SIRL) approach. The proposed stochastic driver behavior model aims to
learn a distribution of cost function, which represents the richness and
uniqueness of human driver behaviors, with a given set of driver-specific
demonstrations. The distributed formation control includes a serial DSMPC with
guaranteed recursive feasibility, closed-loop chance constraint satisfaction,
and string stability. Simulation studies are conducted to investigate the
efficacy of the proposed design under several realistic traffic scenarios.
Compared to the baseline platoon control strategy (deterministic distributed
model predictive control), the proposed DSMPC achieves superior controller
performance in constraint violations and spacing errors.
