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：人导协同自适应巡航控制（HL-CACC）在实际应用中被视为一种极具前景的车辆编队技术。该技术以人工驾驶车辆（HV）作为编队领航车，从而降低系统成本并提升感知与决策的可靠性。然而，现有HL-CACC技术因未充分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。本研究基于随机模型预测控制（SMPC）设计了一种HL-CACC控制器，使其能够预测前方网联人工驾驶车辆（CHV）的驾驶意图。所提出的控制器具备以下特征：i) 在振荡交通流中增强感知安全性；ii) 针对紧急制动提供安全保障；iii) 计算高效，满足实时实施需求。该控制器在PreScan&Simulink联合仿真平台上进行评估，并采用真实车辆轨迹数据对仿真进行校准。结果表明，所提出的控制器：i) 在振荡交通流中将感知安全性提升19.17%；ii) 面对紧急制动时将实际安全性提高7.76%；iii) 被证实具有队列稳定性。在搭载Intel i5-13500H处理器的笔记本电脑上运行时，其计算时间约为3毫秒，证明该控制器具备实时实施条件。