Improving Accident Scene Monitoring With Multisource Data Fusion Under Low-Brightness and Occlusion Conditions

Current road accident survey methodologies often overlook the low-brightness and occlusion conditions, and this study specifically fuses UAV-borne and terrestrial LiDAR data, to facilitate rapid and high-precision accident scene monitoring for these scenarios. Firstly, UAV-borne LiDAR is utilized to acquire point cloud data from the perspective of wide-view. Meanwhile, both the side overlap rate and flight altitude of UAV mapping are considered, and the impacts of them on the accident scene modeling accuracy are explored in low-brightness scenario. Then, the terrestrial LiDAR scanning is adopted to model the accident scene under occlusion condition. Secondly, the Gaussian filtering and voxelization downsampling are conducted to improve the quality of point cloud data and the modeling speed. Thirdly, for enhancing the feature fusion, the Normal Distributions Transform (NDT) algorithm is adopted to register the UAV-borne and terrestrial LiDAR data. Finally, the DJI M300RTK UAV is selected, and two traffic accident scenes of low-brightness and occlusion conditions are conducted. A 50% side overlap rate and a 15m altitude of UAV mapping are recommended, to construct an efficient and accurate accident scene investigation. In addition, compared with the manual taping, the total station measurement and photography method, the monitoring accuracy of the proposed method is optimal with an RMSE of 0.0491. This shows that it is promising to adopt the proposed method to investigate the low-brightness and occlusion traffic accident scenes. Moreover, the limitations and future research directions of the proposed method are presented.