标题： MR-LDM——合并反应纵向决策模型：交互模拟代理的游戏理论人类决策建模 显示英文标题

标题： MR-LDM -- The Merge-Reactive Longitudinal Decision Model: Game Theoretic Human Decision Modeling for Interactive Sim Agents

摘要： 增强仿真环境以复制现实世界驾驶员行为，即更类人的模拟代理，对于开发自动驾驶技术至关重要。 在高速公路合并的背景下，之前的研究已经研究了滞后车辆在高速入口处对合并车辆的运行级别让行动态。 其他专注于战术决策建模的研究通常考虑有限的动作集，或使用具有大量参数集和有限收益范围的收益函数。 在本工作中，我们旨在通过针对具有改进收益函数和滞后动作的战术决策博弈论模型来改进高速公路合并场景的仿真。 我们将此与底层动态模型结合，以获得一个统一的决策和动态模型，该模型能够捕捉合并交互，并以可解释和可理解的方式模拟更真实的交互。 所提出的模型在真实数据集上验证时表现出良好的复杂交互再现能力。 该模型最终被集成到高保真仿真环境中，并确认其计算时间效率足够用于大规模仿真，以支持自动驾驶汽车的发展。 显示英文摘要

摘要： Enhancing simulation environments to replicate real-world driver behavior, i.e., more humanlike sim agents, is essential for developing autonomous vehicle technology. In the context of highway merging, previous works have studied the operational-level yielding dynamics of lag vehicles in response to a merging car at highway on-ramps. Other works focusing on tactical decision modeling generally consider limited action sets or utilize payoff functions with large parameter sets and limited payoff bounds. In this work, we aim to improve the simulation of the highway merge scenario by targeting a game theoretic model for tactical decision-making with improved payoff functions and lag actions. We couple this with an underlying dynamics model to have a unified decision and dynamics model that can capture merging interactions and simulate more realistic interactions in an explainable and interpretable fashion. The proposed model demonstrated good reproducibility of complex interactions when validated on a real-world dataset. The model was finally integrated into a high fidelity simulation environment and confirmed to have adequate computation time efficiency for use in large-scale simulations to support autonomous vehicle development.