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 approac...
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IEEE
2024-01-01
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| author | Shih-Lin Lin Yi-Hsuan Chen |
| author_facet | Shih-Lin Lin Yi-Hsuan Chen |
| author_sort | Shih-Lin Lin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2c0f7d07e75341198cf0eb4c7005d04f |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-2c0f7d07e75341198cf0eb4c7005d04f2025-08-20T02:07:20ZengIEEEIEEE Access2169-35362024-01-011217450717451810.1109/ACCESS.2024.349572110750172Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point AlgorithmShih-Lin Lin0https://orcid.org/0000-0002-0234-8786Yi-Hsuan Chen1Graduate Institute of Vehicle Engineering, National Changhua University of Education, Changhua City, TaiwanGraduate Institute of Vehicle Engineering, National Changhua University of Education, Changhua City, TaiwanWith 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.https://ieeexplore.ieee.org/document/10750172/LiDAR technologyhigh-precision scansvehicle damage assessmentiterative closest point (ICP) algorithmautomated photogrammetry |
| spellingShingle | Shih-Lin Lin Yi-Hsuan Chen Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm IEEE Access LiDAR technology high-precision scans vehicle damage assessment iterative closest point (ICP) algorithm automated photogrammetry |
| title | Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm |
| title_full | Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm |
| title_fullStr | Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm |
| title_full_unstemmed | Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm |
| title_short | Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm |
| title_sort | technique on vehicle damage assessment after collisions using optical radar technology and iterative closest point algorithm |
| topic | LiDAR technology high-precision scans vehicle damage assessment iterative closest point (ICP) algorithm automated photogrammetry |
| url | https://ieeexplore.ieee.org/document/10750172/ |
| work_keys_str_mv | AT shihlinlin techniqueonvehicledamageassessmentaftercollisionsusingopticalradartechnologyanditerativeclosestpointalgorithm AT yihsuanchen techniqueonvehicledamageassessmentaftercollisionsusingopticalradartechnologyanditerativeclosestpointalgorithm |