  The timely provision of traffic sign information to drivers is essential for
the drivers to respond, to ensure safe driving, and to avoid traffic accidents
in a timely manner. We proposed a timely visual recognizability quantitative
evaluation method for traffic signs in large-scale transportation environments.
To achieve this goal, we first address the concept of a visibility field to
reflect the visible distribution of three-dimensional (3D) space and construct
a traffic sign Visibility Evaluation Model (VEM) to measure the traffic sign
visibility for a given viewpoint. Then, based on the VEM, we proposed the
concept of the Visual Recognizability Field (VRF) to reflect the visual
recognizability distribution in 3D space and established a Visual
Recognizability Evaluation Model (VREM) to measure a traffic sign visual
recognizability for a given viewpoint. Next, we proposed a Traffic Sign Timely
Visual Recognizability Evaluation Model (TSTVREM) by combining VREM, the actual
maximum continuous visual recognizable distance, and traffic big data to
measure a traffic sign visual recognizability in different lanes. Finally, we
presented an automatic algorithm to implement the TSTVREM model through traffic
sign and road marking detection and classification, traffic sign environment
point cloud segmentation, viewpoints calculation, and TSTVREM model
realization. The performance of our method for traffic sign timely visual
recognizability evaluation is tested on three road point clouds acquired by a
mobile laser scanning system (RIEGL VMX-450) according to Road Traffic Signs
and Markings (GB 5768-1999 in China), showing that our method is feasible and
efficient.
