Detecting distracted driving is important for developing Advanced Driver Assistance Systems and improving road safety. Most of the existing research analyzes drivers directly via video analysis techniques or by measuring cognitive load, however these approaches often require additional sensors to be installed in vehicles or equipped to drivers. Given that most distractions may have a direct influence on drivers’ control of vehicles, this paper proposes a new method to utilize available vehicle kinematic data for detecting distracted driving. The proposed method predicts vehicle kinematics by fusing multiple state–space models that capture different driving motion patterns under normal driving. An online monitoring scheme is developed by using Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts, which detects abnormal mean shifts of lateral speeds and prediction errors of lane positions to provide warnings of distracted driving. A case study is presented based on two naturalistic driving datasets — the Integrated Vehicle-Based Safety Systems (IVBSS) and Safety Pilot Model Deployment (SPMD) datasets.

检测分心驾驶对于开发高级驾驶辅助系统和提高道路安全非常重要。大多数现有研究直接通过视频分析技术或测量认知负荷来分析驾驶员，但是这些方法通常需要在车辆中安装额外的传感器或为驾驶员配备额外的传感器。鉴于大多数分心可能会直接影响驾驶员对车辆的控制，本文提出了一种利用可用车辆运动学数据来检测分心驾驶的新方法。所提出的方法通过融合多个状态空间模型来预测车辆运动学，这些模型在正常驾驶下捕获不同的驾驶运动模式。使用指数加权移动平均线 (EWMA) 和累积总和 (CUSUM) 图表开发了在线监测方案，它检测横向速度的异常平均偏移和车道位置的预测错误，以提供分心驾驶的警告。案例研究基于两个自然驾驶数据集——基于车辆的集成安全系统 (IVBSS) 和安全飞行员模型部署 (SPMD) 数据集。