As autonomous vehicles (AVs) continue to be integrated into public roads, it is inevitable that they will interact with human-driven vehicles (HVs) in a mixed traffic environment. In such traffic scenarios, it is crucial to consider the reactive and uncertain behavior of HVs when developing control strategies for AVs. This paper investigates the safe control of a platoon of AVs interacting with HVs in longitudinal car-following scenarios. To better predict the behavior of HVs, we propose a model that combines a first-principles nominal model with a Gaussian process (GP) learning-based component. Our results show that this model reduces the root mean square error in predicting HV velocity by 35.64\% compared to the nominal model. Utilizing this model, a model predictive control (MPC) strategy, referred to as GP-MPC, is designed to ensure a safe distance between each vehicle in the mixed vehicle platoon. The GP-MPC integrates the uncertainty assessment of the human-driven vehicle model by the GP models into the distance constraint, which enhances safety guarantees in challenging traffic scenarios such as emergency braking. Simulation case studies comparing the proposed GP-MPC against a baseline MPC demonstrate that the GP-MPC achieves superior safety guarantees while enabling more efficient motion behaviors for all vehicles in the mixed vehicle platoon.
翻译:由于自治车辆(AVs)继续被纳入公共道路,因此在混合交通环境中,他们不可避免地会与人驱动车辆(HVs)互动。在这种交通情况中,在制定AV控制战略时,必须考虑HVs的反应性和不确定行为。本文调查了长纵向汽车跟踪情景中与HVs发生互动的一排AVs的安全控制情况。为了更好地预测HVs的行为,我们提出了一个模式,将一等原则名义模型与Gaussian进程(GP)学习基础部分结合起来。我们的结果显示,这一模式在预测HV的速度比名义模式减少35.64<unk> (HVs)的根值平均平方差。利用这一模式,一个称为GP-MPC的模型预测控制(MPC)战略旨在确保混合车辆排中每部车辆的安全距离。GP-MPC模型将人驱动车辆模型的不确定性评估与远程限制结合起来,这将加强具有挑战性的机动车辆流量假设的安全保障,例如,使高级机动车辆(MPC)能够更安全地进行测试,同时将所有机动车辆(MPC)级安全基准对比。</s>