Delineating Sight Occlusions of Head-On Traffic Signboards under Varying Available Sight Distances Using LiDAR Point Clouds

Abstract

It is essential to delineate visual occlusions of head-on traffic signboards under varying available sight distances (ASDs) to improve road safety and guide maintenance. However, the status of sight occlusions in actual road scenarios is unclear because of the lack of an automatic and quantitative evaluation approach. This study presents a new cluster attention traffic-sign occlusion (CASO) method with a cluster attention traffic-sign network (CASNet) to automatically classify road infrastructures, and an occlusion delineating module to dynamically describe the occlusions of head-on traffic signboards using point clouds. CASNet consists of a cluster module to alleviate the interference of redundant object features and an attention module to focus on learning local features of small samples on road scenes. The occlusion delineating module is investigated to rapidly construct a signboard-related oblique cone and dynamically assess occlusions. The sight occlusions change with varying ASDs and are delineated by two indices: the degree of shaded signboard area, and the degree of occlusion volume from the driver’s dynamic perspective to the head-on signboard. Compared with the state-of-the-art networks, the experimental extraction results achieved for signboards show an overall improved performance. The degree of occlusion volumes toward head-on signboards fluctuates between 26.75% and 36.70%, and the degree of shaded areas on signboards increases from 22.75% to 59.63%, with ASDs varying from 20 to 75 m. This research contributes to evaluating road safety in intelligent transportation systems and accurately guiding the allocation of maintenance budgets to the heavily occluded road sections.