In this paper we consider the application of Stackelberg game theory to model discretionary lane-changing in lightly congested highway setting. The fundamental intent of this model, which is parameterized to capture driver disposition (aggressiveness or inattentiveness), is to help with the development of decision-making strategies for autonomous vehicles in ways that are mindful of how human drivers perform the same function on the road (on which have reported elsewhere.) This paper, however, focuses only on the model development and the respective qualitative assessment. This is accomplished in unit test simulations as well as in bulk mode (i.e. using the Monte Carlo methodology), via a limited traffic micro-simulation compared against the NHTSA 100-Car Naturalistic Driving Safety data. In particular, a qualitative comparison shows the relative consistency of the proposed model with human decision-making in terms of producing qualitatively similar proportions of crashes and near crashes as a function of driver inattentiveness (or aggressiveness). While this result by itself does not offer a true quantitative validation of the proposed model, it does demonstrate the utility of the proposed approach in modeling discretionary lane-changing and may therefore be of use in autonomous driving in a manner that is consistent with human decision making on the road.

中文翻译：

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人类驾驶的博弈论模型及其在自主变道中的应用

在本文中，我们考虑了 Stackelberg 博弈论在轻度拥堵高速公路环境中对自主变道进行建模的应用。该模型的基本意图是通过参数化来捕捉驾驶员的性格（攻击性或注意力不集中），以帮助制定自动驾驶汽车的决策策略，同时注意人类驾驶员如何在道路上执行相同的功能（对此已在别处报道过。）然而，本文仅关注模型开发和相应的定性评估。这是通过与 NHTSA 100 汽车自然驾驶安全数据进行比较的有限交通微观模拟，在单元测试模拟以及批量模式（即使用蒙特卡罗方法）中完成的。特别是，定性比较显示了所提出的模型与人类决策的相对一致性，即产生质量相似的碰撞和接近碰撞的比例作为驾驶员注意力不集中（或攻击性）的函数。虽然这个结果本身并没有对所提出的模型提供真正的定量验证，但它确实证明了所提出的方法在模拟任意车道变换中的效用，因此可能以与人类决策一致的方式用于自动驾驶在路上制作。