Driving behavior is considered a unique driving habit of each driver and has a significant impact on road safety. Classifying driving behavior and introducing policies based on the results can reduce the severity of crashes on the road. Roundabouts are particularly interesting because of the interconnected interaction between different road users at the area of roundabouts, which different driving behavior is hypothesized. This study investigates driving behavior at roundabouts in a mixed traffic environment using a data-driven unsupervised machine learning to classify driving behavior at three roundabouts in Germany. We used a dataset of vehicle kinematics to a group of different vehicles and vulnerable road users (VRUs) at roundabouts and classified them into three categories (i.e., conservative, normal, and aggressive). Results showed that most of the drivers proceeding through a roundabout can be mostly classified into two driving styles: conservative and normal because traffic speeds in roundabouts are relatively lower than in other signalized and unsignalized intersections. Results also showed that about 77% of drivers who interacted with pedestrians or cyclists were classified as conservative drivers compared to about 42% of conservative drivers that did not interact or about 51% from all drivers. It seems that drivers tend to behave abnormally as they interact with VRUs at roundabouts, which increases the risk of crashes when an intersection is multimodal. Results of this study could be helpful in improving the safety of roads by allowing policymakers to determine the effective and suitable safety countermeasures. Results will also be beneficial for the Advanced Driver Assistance System (ADAS) as the technology is being deployed in a mixed traffic environment.

翻译：驾驶行为被视为每位驾驶员独特的驾驶习惯，对道路安全具有重要影响。对驾驶行为进行分类并基于结果制定政策，可降低道路交通事故的严重程度。环岛因其区域内不同道路使用者之间的相互交互作用而格外引人关注，这种交互被假设会催生差异化的驾驶行为。本研究采用数据驱动的无监督机器学习方法，对德国三个环岛的混合交通环境中的驾驶行为进行探究。我们利用车辆运动学数据集，对环岛内不同车辆群体及弱势道路使用者（VRU）进行归类，并划分为三种类型（即保守型、正常型和激进型）。结果表明，多数通过环岛的驾驶员主要可归类为两种驾驶风格：保守型和正常型，这是因为环岛内的交通速度相对低于其他信号灯及无信号灯交叉口。研究还显示，约77%与行人或自行车骑行者发生交互的驾驶员被归类为保守型，而相比之下，未发生此类交互的驾驶员中保守型比例约为42%，全体驾驶员中则约为51%。驾驶员在环岛与弱势道路使用者交互时似乎倾向于表现异常，这增加了多模式交叉口的碰撞风险。本研究结果有助于政策制定者确定有效且适宜的安全应对措施，从而改善道路安全。同时，随着先进驾驶员辅助系统（ADAS）技术逐步部署于混合交通环境，这些结果亦对该系统的开发具有裨益。