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:高速公路并入场景中的混合交通条件为智能和联网汽车(CAVs)与进入的人驾驶汽车(HDVs)相互作用带来了重要的建模和控制挑战。在本文中,我们提出了一种学习CAV-HDV相互作用的近似信息状态模型的方法,以便CAV在高速公路合并时能够安全操纵。在我们的方法中,CAV通过使用近似信息状态学习即将到来的HDV的行为,然后生成控制策略来促进合并。首先,我们通过使用该方法验证了从Next-Generation模拟存储库中提取的混合信号情况下的HDV行为的有效性。然后,我们使用标准的反向强化学习方法生成了HDV-CAV交互的模拟数据,在高速公路合并场景中,这些数据涵盖了各种行驶行为,从激进的到保守的。我们展示了近似信息状态模型在不假定生成模型的先验知识的情况下,只使用观测数据就能够学习预测HDV的未来轨迹。随后,我们为CAV生成了安全控制策略,同时合并HDVs,根据不同的行驶行为,从激进到保守。我们通过数值模拟展示了所提出方法的有效性。