  Human-Lead Cooperative Adaptive Cruise Control (HL-CACC) is regarded as a
promising vehicle platooning technology in real-world implementation. By
utilizing a Human-driven Vehicle (HV) as the platoon leader, HL-CACC reduces
the cost and enhances the reliability of perception and decision-making.
However, state-of-the-art HL-CACC technology still has a great limitation on
driving safety for the lack of considering the leading human driver's uncertain
behaving. In this study, a HL-CACC controller is designed based on Stochastic
Model Predictive Control (SMPC). It is enabled to predict the driving intention
of the leading Connected Human-Driven Vehicle (CHV). The proposed controller
has the following features: i) enhanced perceived safety in oscillating
traffic; ii) guaranteed safety against hard brakes; iii) computational
efficient for real-time implementation. The proposed controller is evaluated on
a PreScan&Simulink simulation platform. Real vehicle trajectory data is
collected for the calibration of simulation. Results reveal that the proposed
controller: i) improves perceived safety by 19.17% in oscillating traffic; ii)
enhances actual safety by 7.76% against hard brake; iii) is confirmed with
string stability. The computation time is approximately 3 milliseconds when
running on a laptop equipped with an Intel i5-13500H CPU. This indicates the
proposed controller is ready for real-time implementation.
