Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm

With the rapid advancement of technology, there is an increasing need for quick and accurate analysis of vehicle accident severity, determination of accident liability, assessment of vehicle damage extent, and efficient calculation of insurance compensation. This study proposes an innovative approach for vehicle damage assessment by utilizing smartphones equipped with Light Detection and Ranging (LiDAR) technology. High-precision scans of vehicles are conducted in two stages: an initial comprehensive exterior scan of the undamaged vehicle, followed by a scan of the same model post-collision. The obtained point cloud data is processed using 3D reconstruction techniques to create virtual vehicle models. We apply the Iterative Closest Point (ICP) algorithm and Singular Value Decomposition (SVD) methods, along with a proposed deep learning neural network optimization model, to perform point cloud alignment between the pre-collision and post-collision vehicle models. The proposed method enhances the alignment accuracy by refining the transformation parameters, effectively handling nonlinear deformations caused by collisions. Key performance indicators, including Root Mean Square Error (RMSE), relative translation, and relative rotation angles, are used to quantify the extent of vehicle damage. Experimental results demonstrate that the proposed method significantly reduces the relative rotation from approximately 4.03° to 0.04° and the RMSE from about 1.27 units to 0.29 units compared to the traditional ICP and SVD methods, indicating a substantial improvement in alignment precision and damage assessment accuracy. The contributions of this study lie in integrating LiDAR technology with advanced point cloud processing algorithms and a deep learning optimization model for vehicle damage assessment, demonstrating high precision and cost-effectiveness. This method not only enhances the accuracy and efficiency of damage assessment but also reduces costs, offering significant practical value for rapid accident evaluation and insurance claims processing. By providing improved accuracy and reliability in damage assessments, this study significantly contributes to the fields of automotive safety, insurance, and repair.