On-Road Vehicle Trajectory Collection and Scene-Based Lane Change Analysis: Part I

• Published in: IEEE transactions on intelligent transportation systems Vol. 18; no. 1; pp. 192 - 205
• Main Authors: Huijing Zhao, Chao Wang, Yuping Lin, Guillemard, Franck, Geronimi, Stephane, Aioun, Francois
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data models; Lane changing; Laser radar; Lidar; Lidar processing; Microscopy; Moving object recognition; on-road; Roads; Scene analysis; Sensors; tactical driving behavior; Trajectory; Trajectory analysis; trajectory collection; vehicle; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2016.2571726

This two-part paper aims to study lane change behaviors at the tactical level from an on-road perspective, with a special focus on analyzing the interactions between an ego and surrounding vehicles during the procedure. Part I addresses vehicle trajectory collection, whereas Part II addresses lane change extraction and scene-based behavioral analysis. Different from the general technique of moving object detection and tracking, trajectory collection for tactical driving behavior study is required to have the properties of consistency, completeness, continuity, and accuracy. This paper proposes a system of on-road vehicle trajectory collection, where an instrumented vehicle is developed with multiple horizontal 2-D lidars that have 360° coverage. The software is developed by fitting the laser points of all lidars on a vehicle model using a coupled estimation of features and reliability along frames to achieve accurate state estimations of occluded data and robust data association in multiviewpoint sensing. The performance is investigated extensively, and a large trajectory set is developed through on-road driving at the Fourth Ring Road in Beijing for a total distance of 64 km, with more than 5700 environmental trajectories with a total length of over 19 h. The performance is demonstrated to be of high quality in terms of the required properties. To the authors' knowledge, this is the first system that is able to automatically collect all-around vehicle trajectories during on-road driving and to demonstrate good performance in providing a high-quality database for driving behavior studies from an on-road perspective that addresses vehicle interactions in real-world traffic at the trajectory level.