In today's rapidly evolving urban landscapes, efficient and accurate mapping
of road infrastructure is critical for optimizing transportation systems,
enhancing road safety, and improving the overall mobility experience for
drivers and commuters. Yet, a formidable bottleneck obstructs progress - the
laborious and time-intensive manual identification of intersections. Simply
considering the shear number of intersections that need to be identified, and
the labor hours required per intersection, the need for an automated solution
becomes undeniable. To address this challenge, we propose a novel approach that
leverages connected vehicle data and cutting-edge deep learning techniques. By
employing geohashing to segment vehicle trajectories and then generating image
representations of road segments, we utilize the YOLOv5 (You Only Look Once
version 5) algorithm for accurate classification of both straight road segments
and intersections. Experimental results demonstrate an impressive overall
classification accuracy of 95%, with straight roads achieving a remarkable 97%
F1 score and intersections reaching a 90% F1 score. This approach not only
saves time and resources but also enables more frequent updates and a
comprehensive understanding of the road network. Our research showcases the
potential impact on traffic management, urban planning, and autonomous vehicle
navigation systems. The fusion of connected vehicle data and deep learning
models holds promise for a transformative shift in road infrastructure mapping,
propelling us towards a smarter, safer, and more connected transportation
ecosystem.

通过利用车联网数据和深度学习模型，在道路基础设施映射方面取得了显著的成果，提出了一种基于 geohashing 和 YOLOv5 算法的自动识别路口的新方法，实验证明了该方法的高准确性和广泛应用前景。