An open problem in autonomous driving research is modeling human driving behavior, which is needed for the planning component of the autonomy stack, safety validation through traffic simulation, and causal inference for generating explanations for autonomous driving. Modeling human driving behavior is challenging because it is stochastic, high-dimensional, and involves interaction between multiple agents. This problem has been studied in various communities with a vast body of literature. Existing reviews have generally focused on one aspect: motion prediction. In this article, we present a unification of the literature that covers intent estimation, trait estimation, and motion prediction. This unification is enabled by modeling multi-agent driving as a partially observable stochastic game, which allows us to cast driver modeling tasks as inference problems. We classify driver models into a taxonomy based on the specific tasks they address and the key attributes of their approach. Finally, we identify open research opportunities in the field of driver modeling.

翻译：自动驾驶研究中的一个开放性问题是如何对人类驾驶行为进行建模，该问题在自动驾驶系统的规划模块、通过交通仿真进行安全验证以及为自动驾驶生成解释的因果推断中均具有关键意义。人类驾驶行为的建模具有挑战性，因其具有随机性、高维性且涉及多智能体间的交互。该问题已在多个研究领域得到广泛探讨，相关文献浩如烟海。现有综述通常聚焦于单一方向：运动预测。本文提出了一种涵盖意图估计、特性估计与运动预测的文献统一框架。该统一框架通过将多智能体驾驶建模为部分可观测随机博弈而实现，使我们能够将驾驶员建模任务转化为推理问题。我们根据模型所解决的具体任务及其方法的核心属性，将驾驶员模型进行分类，构建了一个分类体系。最后，我们指出了驾驶员建模领域中待解决的研究机遇。