标题： 基于双模型预测路径积分控制的交互感知自动驾驶高速匝道并道主动学习 显示英文标题

标题： Active Learning with Dual Model Predictive Path-Integral Control for Interaction-Aware Autonomous Highway On-ramp Merging

摘要： 将自动驾驶车辆合并到密集的高速公路交通中是一个复杂的决策任务，其中车辆必须识别一个潜在的空隙并与周围的驾驶员协调，每个驾驶员可能表现出不同的驾驶行为。 许多现有方法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来捕捉人类驾驶员的完整意图。 在本文中，我们提出了一种基于模型预测路径积分控制的新型双控制框架，以生成交互轨迹。 该框架采用贝叶斯推理方法，主动学习代理参数，即其他驾驶员的模型参数。 所提出的框架采用一种基于采样的方法，通过使用GPU，适合实时实现。 我们通过在高保真和低保真仿真场景中进行的全面数值模拟，展示了所提出方法的有效性，重点关注自动驾驶车辆的并道合并。 显示英文摘要

摘要： 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.