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.

翻译：人领协同自适应巡航控制（HL-CACC）被视为一种在现实应用中具有前景的车辆队列技术。通过使用人类驾驶车辆（HV）作为队列领导者，HL-CACC降低了成本并增强了感知与决策的可靠性。然而，现有HL-CACC技术由于未考虑领航人类驾驶员的不确定行为，在行驶安全性方面仍存在显著局限性。本研究基于随机模型预测控制（SMPC）设计了一种HL-CACC控制器。该控制器能够预测领航网联人类驾驶车辆（CHV）的驾驶意图。所提出的控制器具有以下特点：i) 在振荡交通中增强感知安全性；ii) 保证急刹车情况下的安全性；iii) 计算高效，适用于实时实现。该控制器在PreScan&Simulink仿真平台上进行了评估。为校准仿真，收集了真实车辆轨迹数据。结果表明，所提出的控制器：i) 在振荡交通中将感知安全性提升了19.17%；ii) 在急刹车情况下将实际安全性提升了7.76%；iii) 被确认具有队列稳定性。在配备Intel i5-13500H CPU的笔记本电脑上运行时，计算时间约为3毫秒，这表明所提出的控制器已具备实时实现的条件。