Accid Anal Prev. 2026 Feb 26;230:108479. doi: 10.1016/j.aap.2026.108479. Online ahead of print.
ABSTRACT
Road traffic safety assessment is critical for mitigating traffic accidents, safeguarding human life and property, and fostering socioeconomic development. Existing methods, which rely on the statistical analysis of historical traffic accidents and conflicts as well as the evaluation of road design parameters, play a pivotal role in assessing road traffic safety. Driving visibility acts as a critical indicator of the driver’s field of view and serves as a significant supplement to these methods. Consequently, this study proposes a method for quantifying 3D driving visibility utilizing LiDAR point cloud data. The approach establishes a computational framework for 3D visible space from the driver’s perspective and introduces a novel Driving Visibility Index (DVI) to enable visibility-based safety evaluation. The proposed method consists of three primary components: road point cloud acquisition and preprocessing, driving visibility field computation, and DVI computation. We validated the proposed method along Yixian Avenue at Sun Yat-sen University’s Zhuhai Campus, generating a driving safety map. The results revealed that the overall DVI for bidirectional travel on Yixian Avenue ranges from 0.2 to 0.6, indicating suboptimal safety conditions. Further comparative analysis with field-collected data subsequently confirmed the robustness of our proposed method. The proposed method’s objective and intuitive quantification of 3D visible space from the driver’s perspective provides a novel basis for traffic management, with significant applications spanning road design, traffic facility layout, and the validation of intelligent transportation networks.
PMID:41762446 | DOI:10.1016/j.aap.2026.108479
