As autonomous vehicles (AVs) become more prevalent on public roads, they will inevitably interact with human-driven vehicles (HVs) in mixed traffic scenarios. To ensure safe interactions between AVs and HVs, it is crucial to account for the uncertain behaviors of HVs when developing control strategies for AVs. In this paper, we propose an efficient learning-based modeling approach for HVs that combines a first-principles model with a Gaussian process (GP) learning-based component. The GP model corrects the velocity prediction of the first-principles model and estimates its uncertainty. Utilizing this model, a model predictive control (MPC) strategy, referred to as GP-MPC, was designed to enhance the safe control of a mixed vehicle platoon by integrating the uncertainty assessment into the distance constraint. We compare our GP-MPC strategy with a baseline MPC that uses only the first-principles model in simulation studies. We show that our GP-MPC strategy provides more robust safe distance guarantees and enables more efficient travel behaviors (higher travel speeds) for all vehicles in the mixed platoon. Moreover, by incorporating a sparse GP technique in HV modeling and a dynamic GP prediction in MPC, we achieve an average computation time for GP-MPC at each time step that is only 5% longer than the baseline MPC, which is approximately 100 times faster than our previous work that did not use these approximations. This work demonstrates how learning-based modeling of HVs can enhance safety and efficiency in mixed traffic involving AV-HV interaction.

翻译：随着自动驾驶汽车（Autonomous Vehicles, AVs）在公共道路上日益普及，它们将不可避免地与人类驾驶车辆（Human-driven Vehicles, HVs）在混合交通场景中产生交互。为确保AV与HV之间的安全交互，在制定AV控制策略时，必须考虑HVs的不确定性行为。本文提出了一种高效的基于学习的HV建模方法，该方法将第一性原理模型与基于高斯过程（Gaussian Process, GP）的学习组件相结合。GP模型能够校正第一性原理模型的速度预测，并估算其不确定性。基于该模型，我们设计了一种模型预测控制（Model Predictive Control, MPC）策略（称为GP-MPC），通过将不确定性评估融入距离约束，增强混合车队的安全控制。在仿真研究中，我们将GP-MPC策略与仅使用第一性原理模型的基准MPC进行对比。结果表明，我们的GP-MPC策略能够提供更稳健的安全距离保障，并实现混合车队中所有车辆更高效的行驶行为（更高的行驶速度）。此外，通过在HV建模中引入稀疏GP技术以及MPC中的动态GP预测，我们实现了GP-MPC每个时间步的平均计算时间仅比基准MPC增加5%，且相比未采用这些近似方法的先前工作，速度提升约100倍。本研究展示了基于学习的HV建模如何提升涉及AV-HV交互的混合交通场景的安全性与效率。