In this paper, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The need for a modeling framework that can address multiple human-human and human-automation interactions, where all the agents can be modeled as decision makers simultaneously, is the main motivation behind this work. Such a modeling framework may be utilized for the validation and verification of autonomous vehicles: It is estimated that for an autonomous vehicle to reach the same safety level of cars with drivers, millions of miles of driving tests are required. The modeling framework presented in this paper may be used in a high-fidelity traffic simulator consisting of multiple human decision makers to reduce the time and effort spent for testing by allowing safe and quick assessment of self-driving algorithms. To demonstrate the fidelity of the proposed modeling framework, game theoretical driver models are compared with real human driver behavior patterns extracted from traffic data.

中文翻译：

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通过深度强化学习和行为博弈论进行驾驶员建模

在本文中，提出了深度强化学习和分层博弈论的协同组合，作为高速公路驾驶场景中驾驶员行为预测的建模框架。这项工作背后的主要动机是需要一个可以解决多种人与人和人与自动化交互的建模框架，其中所有代理都可以同时建模为决策者。这样的建模框架可用于自动驾驶汽车的验证和验证：据估计，自动驾驶汽车要达到与驾驶员汽车相同的安全水平，需要进行数百万英里的驾驶测试。本文提出的建模框架可用于由多个人类决策者组成的高保真交通模拟器，通过允许安全快速地评估自动驾驶算法来减少测试所花费的时间和精力。为了证明所提出的建模框架的保真度，将博弈论驾驶员模型与从交通数据中提取的真实人类驾驶员行为模式进行了比较。