Insight into individual driving behavior and habits is essential in traffic operation, safety, and energy management. With Connected Vehicle (CV) technology aiming to address all three of these, the identification of driving patterns is a necessary component in the design of personalized Advanced Driver Assistance Systems (ADAS) for CVs. Our study aims to address this need by taking a unique approach to analyzing bidirectional (i.e. longitudinal and lateral) control features of drivers, using a simple rule-based classification process to group their driving behaviors and habits. We have analyzed high resolution driving data from the real-world CV-testbed, Safety Pilot Model Deployment, in Ann Arbor, Michigan, to identify diverse driving behavior on freeway, arterial, and ramp road types. Using three vehicular features known as jerk, leading headway, and yaw rate, driving characteristics are classified into two groups (Safe Driving and Hostile Driving) on short-term classification, and drivers habits are categorized into three classes (Calm Driver, Rational Driver, and Aggressive Driver). Proposed classification models are tested on unclassified datasets to validate the model conviction regarding speeding and steep acceleration. Through the proposed method, behavior classification has been successfully identified about 90 percent of speeding and similar level of acute acceleration instances. In addition, our study advances an ADAS interface that interacts with drivers in real-time in order to transform information about driving behaviors and habits into feedback to individual drivers. We propose an adaptive and flexible classification approach to identify both short-term and long-term driving behavior from naturalistic driving data to identify and, eventually, communicate adverse driving behavioral patterns.

翻译：洞察个体驾驶行为与习惯对交通运行、安全及能源管理至关重要。随着旨在解决这三方面问题的网联车辆技术发展，驾驶模式识别成为设计个性化高级驾驶辅助系统的必要组成部分。本研究通过创新性地分析驾驶员双向（即纵向与横向）控制特征，采用基于简单规则的分类流程对其驾驶行为与习惯进行分组，以应对这一需求。我们分析了密歇根州安娜堡市真实路测平台"安全试点模型部署"的高分辨率驾驶数据，识别了高速公路、主干道及匝道场景下的多样化驾驶行为。基于 jerk（加加速度）、leading headway（前车间距）和 yaw rate（横摆角速度）三项车辆特征，短期分类将驾驶特征分为两类（安全驾驶与激烈驾驶），长期分类将驾驶员习惯归为三类（冷静型、理性型与激进型）。所提分类模型在未标注数据集上进行了测试，以验证其对超速与急加速行为的识别置信度。通过本方法，行为分类成功识别了约90%的超速行为及相近比例的急加速事件。此外，我们开发了能与驾驶员实时交互的高级驾驶辅助系统界面，将驾驶行为与习惯信息转化为向个体驾驶员的反馈。本研究提出了一种自适应且灵活的分类方法，可从自然驾驶数据中识别短期与长期驾驶行为，最终对异常驾驶行为模式进行识别与反馈。