基于双向驾驶特征的高级驾驶辅助系统短期和长期驾驶行为分类
The Classification of Short and Long-term Driving Behavior for an Advanced Driver Assistance System by Analyzing Bidirectional Driving Features
摘要：
洞察个人驾驶行为和习惯对于交通运营、安全和能源管理至关重要。随着互联车辆（CV）技术旨在解决所有这三个问题，驾驶模式的识别是为CV设计个性化高级驾驶员辅助系统（ADAS）的必要组成部分。我们的研究旨在通过采用一种独特的方法来分析驾驶员的双向（即纵向和横向）控制特征，使用一个简单的基于规则的分类过程来对他们的驾驶行为和习惯进行分组，从而满足这一需求。我们分析了来自密歇根州安娜堡的真实CV测试平台Safety Pilot Model Deployment的高分辨率驾驶数据，以识别高速公路，主干道和坡道道路类型上的各种驾驶行为。使用三个车辆特征，称为加加速度，领先车头时距，和横摆率，驾驶特性分为两组（安全驾驶和敌意驾驶）的短期分类，和驾驶员的习惯分为三类（冷静驾驶员，理性驾驶员，和侵略性驾驶员）。建议的分类模型进行了测试，未分类的数据集，以验证模型的信念，超速和陡峭的加速度。通过所提出的方法，行为分类已经成功地识别出了约90%的超速和类似水平的急性加速度实例。此外，我们的研究还提出了一个ADAS接口，该接口与驾驶员进行实时交互，以便将有关驾驶行为和习惯的信息转化为对单个驾驶员的反馈。我们提出了一种自适应和灵活的分类方法来识别短期和长期的驾驶行为，从自然驾驶数据识别，并最终传达不利的驾驶行为模式。
英文摘要：
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.
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