An integrated solution for lane level irregular driving detection on highways. (July 2015)

• Record Type: Journal Article
• Authors: Sun, Rui; Ochieng, Washington Yotto; Feng, Shaojun
• Is Part Of: Transportation research. Volume 56(2015)
• Page Start: 61
• Page End: 79
• Publication Date: 2015-07
• Subjects: ITS -- Irregular driving -- Sensor integration -- Fuzzy inference system; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2015.03.036
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: An integrated solution is designed for lane level positioning. A fuzzy inference system is designed for driving pattern classification. Particle Filter model performs better than Extended Kalman Filter model. Correct detection rate increases with output rate decreases. Abstract: Global Navigation Satellite Systems (GNSS) has been widely used in the provision of Intelligent Transportation System (ITS) services. Current meter level system availability can fulfill the road level applications, such as route guide, fleet management and traffic control. However, meter level of system performance is not sufficient for the advanced safety applications. These lane level safety applications requires centimeter/decimeter positioning accuracy, with high integrity, continuity and availability include lane control, collision avoidance and intelligent speed assistance, etc. Detecting lane level irregular driving behavior is the basic requirement for these safety related ITS applications. The two major issues involved in the lane level irregular driving identification are accessing to high accuracy positioning and vehicle dynamic parameters and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated solution for the lane level irregular driving detection. Access to high accuracy positioning is enabled by GNSS and Inertial Navigation System (INS) integration using filtering with precise vehicle motion models and lane information. The detection of different types of irregular driving behaviour is based on the application of a Fuzzy Inference System (FIS). The evaluation of the designed integrated systems in the field test shows that 0.5 m accuracy positioning source is required for lane level irregular driving detection algorithm and the designed system can detect irregular driving styles.