Highway merging scenarios featuring mixed traffic conditions pose significant modeling and control challenges for connected and automated vehicles (CAVs) interacting with incoming on-ramp human-driven vehicles (HDVs). In this paper, we present an approach to learn an approximate information state model of CAV-HDV interactions for a CAV to maneuver safely during highway merging. In our approach, the CAV learns the behavior of an incoming HDV using approximate information states before generating a control strategy to facilitate merging. First, we validate the efficacy of this framework on real-world data by using it to predict the behavior of an HDV in mixed traffic situations extracted from the Next-Generation Simulation repository. Then, we generate simulation data for HDV-CAV interactions in a highway merging scenario using a standard inverse reinforcement learning approach. Without assuming a prior knowledge of the generating model, we show that our approximate information state model learns to predict the future trajectory of the HDV using only observations. Subsequently, we generate safe control policies for a CAV while merging with HDVs, demonstrating a spectrum of driving behaviors, from aggressive to conservative. We demonstrate the effectiveness of the proposed approach by performing numerical simulations.

翻译：高速公路合流场景中存在混合交通条件，这给与匝道人工驾驶车辆（HDV）交互的网联与自动驾驶车辆（CAV）带来了显著的建模与控制挑战。本文提出了一种方法，通过学习CAV与HDV交互的近似信息状态模型，使CAV在高速公路合流过程中能够安全机动。在该方法中，CAV利用近似信息状态学习即将驶入的HDV行为，随后生成合流控制策略。首先，我们利用下一代仿真数据库提取的混合交通场景数据，通过预测HDV行为验证该框架在真实数据上的有效性。接着，我们采用标准逆强化学习方法生成高速公路合流场景中HDV-CAV交互的仿真数据。在不假设生成模型先验知识的情况下，研究表明，我们的近似信息状态模型仅通过观测即可学习预测HDV的未来轨迹。随后，我们为与HDV合流过程中的CAV生成安全控制策略，展现了从激进到保守的多种驾驶行为。通过数值仿真验证了所提方法的有效性。