VT-Lane: An Exploratory Study of an Ad-hoc Framework for Real-time Intersection Turn Count and Trajectory Reconstruction Using NEMA Phases-Based Virtual Traffic Lanes

In this study, we propose an ad-hoc framework for real-time turn count and trajectory reconstruction for vehicles passing through urban intersections. Our proposed framework utilizes virtual lanes representing the 8 standard National Electrical Manufacturers Association (NEMA) movements within an intersection. A Python Graphical User Interface (GUI) was developed is utilized to identify entry planes for each NEMA movement, obtaining the trajectories and counts for the vehicles that are detected and identified at those planes which are then used as identifiers for other vehicles detected inside the intersection using a nearest neighbors search algorithm. Our proposed framework runs as an additional layer to any multi-object tracker with minimal additional computation, and the results of this preliminary assessment indicate the high potential that this proposed framework has in obtaining reliable turn count and in mitigating identity switches by resolving the vehicle re-identification that occurs within the intersection due to detection errors and occlusion, resulting in more accurate vehicle trajectories from video data which will aid developing more reliable intersection safety surrogate measures.