Online distraction detection for naturalistic driving dataset using kinematic motion models and a multiple model algorithm. (September 2021)

• Record Type: Journal Article
• Authors: Sun, Wenbo; Aguirre, Matthew; Jin, Jionghua (Judy); Feng, Fred; Rajab, Samer; Saigusa, Shigenobu; Dsa, Jovin; Bao, Shan
• Is Part Of: Transportation research. Volume 130(2021)
• Publication Date: 2021-09
• Subjects: Distraction detection -- Advanced driver assistance system -- Vehicle kinematic data -- Control chart -- Model fusion; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103317
• Languages: English
• ISSNs: 0968-090X

Abstract

Abstract: 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. Highlights: Propose a general methodology to detect distracted behaviors based on vehicle kinematic data. Implement the proposed method in ADAS to enhance driving safety. Fuse multiple state–space models to track kinematic signals during normal driving. Detect abnormal driving behaviors based on statistical quality control charts. Validate the method using two naturalistic datasets (IVBSS & SPMD).