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：随着自动驾驶车辆（AVs）逐步融入公共道路，它们不可避免地会在混合交通环境中与人类驾驶车辆（HVs）产生交互。在此类交通场景中，制定AV控制策略时必须考虑HVs的响应性及不确定性行为。本文研究了纵向跟车场景下，AV队列与HVs交互时的安全控制问题。为更准确预测HVs行为，我们提出了一种融合第一原理标称模型与高斯过程（GP）学习组件的混合模型。结果表明，与标称模型相比，该模型预测HV速度的均方根误差降低了35.64%。基于此模型，我们设计了一种模型预测控制（MPC）策略（命名为GP-MPC），以确保混合车辆队列中各车间距的安全性。GP-MPC将GP模型对HVs驾驶行为的不确定性评估纳入间距约束中，从而在紧急制动等复杂交通场景下增强了安全保障。仿真案例研究表明，与基准MPC相比，所提出的GP-MPC在提供更优安全保证的同时，能够实现混合车辆队列中所有车辆更高效的运动行为。