Merging into dense highway traffic for an autonomous vehicle is a complex decision-making task, wherein the vehicle must identify a potential gap and coordinate with surrounding human drivers, each of whom may exhibit diverse driving behaviors. Many existing methods consider other drivers to be dynamic obstacles and, as a result, are incapable of capturing the full intent of the human drivers via this passive planning. In this paper, we propose a novel dual control framework based on Model Predictive Path-Integral control to generate interactive trajectories. This framework incorporates a Bayesian inference approach to actively learn the agents' parameters, i.e., other drivers' model parameters. The proposed framework employs a sampling-based approach that is suitable for real-time implementation through the utilization of GPUs. We illustrate the effectiveness of our proposed methodology through comprehensive numerical simulations conducted in both high and low-fidelity simulation scenarios focusing on autonomous on-ramp merging.

翻译：自主车辆在密集高速公路交通流中完成合流是一项复杂的决策任务，车辆需识别潜在间隙并与周围具有不同驾驶行为的人类驾驶员协调。现有方法大多将其他驾驶员视为动态障碍物，因此无法通过这种被动规划充分捕捉人类驾驶员的完整意图。本文提出一种基于模型预测路径积分控制的新型双控制框架，用于生成交互式轨迹。该框架融合贝叶斯推断方法，主动学习智能体参数（即其他驾驶员的模型参数）。所提框架采用基于采样的方法，通过利用GPU实现实时部署。我们通过高保真度与低保真度两种仿真场景下的数值实验，验证了该方法在自主匝道合流通用中的有效性。