论文标题
用于建模和预测人类驾驶员行为的算法的分类和审查
A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior
论文作者
论文摘要
我们提出了有关驾驶员行为建模的文献中200个模型的评论和分类学。我们首先引入一个数学框架来描述交互式多主体流量的动态。基于部分可观察到的随机游戏，该框架为讨论不同的驱动程序建模技术提供了基础。我们的分类法是围绕国家估计，意图估计，特质估计和运动预测的核心建模任务构建的，还讨论了风险估计，异常检测，行为模仿和显微镜交通模拟的辅助任务。现有的驱动程序模型是根据他们解决的特定任务和方法的关键属性进行分类的。
We present a review and taxonomy of 200 models from the literature on driver behavior modeling. We begin by introducing a mathematical framework for describing the dynamics of interactive multi-agent traffic. Based on the partially observable stochastic game, this framework provides a basis for discussing different driver modeling techniques. Our taxonomy is constructed around the core modeling tasks of state estimation, intention estimation, trait estimation, and motion prediction, and also discusses the auxiliary tasks of risk estimation, anomaly detection, behavior imitation and microscopic traffic simulation. Existing driver models are categorized based on the specific tasks they address and key attributes of their approach.
