论文标题
通过深度强化学习和行为游戏理论进行驾驶员建模
Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory
论文作者
论文摘要
在本文中，提出了深入强化学习和分层游戏理论的协同组合，作为在高速公路驾驶场景中对驾驶员进行行为预测的建模框架。对可以解决多个人类和人类自动化相互作用的建模框架的需求，可以同时将所有代理人同时建模为决策者，这是这项工作的主要动机。这种建模框架可以用于验证和验证自动驾驶汽车：据估计，要使自动驾驶汽车达到具有驾驶员的汽车安全水平，需要数百万英里的驾驶测试。本文介绍的建模框架可用于高保真交通模拟器中，该模拟器由多个人类决策者组成，以减少对自动驾驶算法的安全，快速评估进行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，将游戏理论驱动程序模型与从流量数据中提取的实际人类驾驶员行为模式进行了比较。
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.
