Driving style is usually used to characterize driving behavior for a driver or a group of drivers. However, it remains unclear how one individual's driving style shares certain common grounds with other drivers. Our insight is that driving behavior is a sequence of responses to the weighted mixture of latent driving styles that are shareable within and between individuals. To this end, this paper develops a hierarchical latent model to learn the relationship between driving behavior and driving styles. We first propose a fragment-based approach to represent complex sequential driving behavior, allowing for sufficiently representing driving behavior in a low-dimension feature space. Then, we provide an analytical formulation for the interaction of driving behavior and shareable driving style with a hierarchical latent model by introducing the mechanism of Dirichlet allocation. Our developed model is finally validated and verified with 100 drivers in naturalistic driving settings with urban and highways. Experimental results reveal that individuals share driving styles within and between them. We also analyzed the influence of personalities (e.g., age, gender, and driving experience) on driving styles and found that a naturally aggressive driver would not always keep driving aggressively (i.e., could behave calmly sometimes) but with a higher proportion of aggressiveness than other types of drivers.

翻译：驾驶风格通常用于描述单个驾驶员或一组驾驶员的驾驶行为特征。然而，个体驾驶风格如何与其他驾驶员存在共同基础仍不明确。我们的核心观点是：驾驶行为是对个体内部及个体间可共享的潜驾驶风格加权混合序列响应。为此，本文提出一种分层潜模型，以学习驾驶行为与驾驶风格之间的关联。首先，我们提出基于片段的方法来表征复杂的序列驾驶行为，能够在低维特征空间中充分表达驾驶行为。其次，通过引入狄利克雷分配机制，我们给出了驾驶行为与可共享驾驶风格交互作用的分层潜模型分析框架。最终在包含城市道路与高速公路的自然驾驶场景中，对100名驾驶员进行了模型验证。实验结果表明，个体内部及个体间存在共享的驾驶风格。我们还分析了人格特征（如年龄、性别、驾驶经验）对驾驶风格的影响，发现天生攻击型驾驶员并非始终保持激进驾驶（即有时会表现平静），但其攻击性驾驶行为的比例仍高于其他类型驾驶员。