Abstract
Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.
Highlights
Traffic signs are an important kind of transportation facility that present traffic information, such as speed and driver behavior restrictions, road changes ahead, and so forth
We present a quantitative Traffic Sign Visual Recognizability Evaluation Model (TSVREM), and propose an automatic TSVREM calculation approach based on Mobile Laser Scanning (MLS) 3D point clouds
To better understand the TSVREM model, we propose a visibility field concept as follows: Visibility field: For a given surrounding around a target object, the visibility distribution of viewpoints in a 3D space constitutes a visibility field
Summary
Traffic signs are an important kind of transportation facility that present traffic information, such as speed and driver behavior restrictions, road changes ahead, and so forth. How to accurately and efficiently evaluate the visibility and recognizability of traffic signs in a large-scale traffic environment is a challenging problem. Existing research on the evaluation of traffic sign visibility and recognizability is based mainly on simulator and image methods and naturalistic driving experimentation. Simulator based methods [8,9,10] cannot evaluate the visibility and recognizability of real roads. Mobile Laser Scanning (MLS) systems scan large-scale road environments at normal driving speeds and collect highly accurate 3D point clouds over the area of driving. We present a quantitative Traffic Sign Visual Recognizability Evaluation Model (TSVREM), and propose an automatic TSVREM calculation approach based on MLS 3D point clouds. To the best of our knowledge, this is the first solution for accurate traffic sign visual recognizability evaluation over a large-scale traffic environment. This paper is organized as follows: Section 2 reviews the previous work; Section 3 defines the TSVREM model; Section 4 gives the automatic calculation of TSVREM on MLS point clouds; Section 5 describes the experiments; and Section 6 concludes the paper
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