# Fast and High Precise Spatial Documentation of Traffic Accident Site Using Only Low-Cost RPAS
###### Abstract  
Traffic accident documentation worldwide is generally done by conventional methods (e.g., surveying wheel and steel tapeline) followed by sketch and plan creation. These conventional methods are time-consuming and inaccurate; therefore, they can possibly be replaced by advanced modern methods, using the remotely piloted aircraft system (RPAS) for obtaining data, as well as the structure from motion (SfM) for processing. For the proof of concept, we designed and performed an experiment using low-cost multi-rotor RPAS on an arranged traffic accident involving three cars. The result of this experiment was an accurate 3D model with the possibility of precise measurement. The differences in distances and reference objects' dimensions were in general below 1% compared to real values. The presented method is time-saving (requiring 85% less time), more precise, and more efficient compared to conventional methods. Moreover, it allows better data reusability, thus is very suitable and can be recommended for usage in routine situations in traffic accident documentation. RPAS; traffic accident documentation; aerial photogrammetry; 3D model +
Footnote †: 2021: 11, 4013. [[https://doi.org/10.3390/app11094013](https://doi.org/10.3390/app11094013)]([https://doi.org/10.3390/app11094013](https://doi.org/10.3390/app11094013))  
tation. The authors compared many setting parameters, and their influences on accuracy (overlaps of images, number of images, quality of dense point cloud, mesh processing, etc.). Three ground control points (GCPs), obtained from the global navigation satellite system (GNSS), enter the photogrammetric image blocks processing. The authors of [5] published the article \"UAV Image Mosaic for Road Traffic Accident Scene\", describing RPAS image mosaic technology, which includes the utilization of image zooming, scale invariant feature transform (SIFT) feature point extraction, SIFT feature matching, and random sample consensus (RANSAC) feature point purification. The last step of the process is image fusion. The authors claimed that it is possible to measure distances on processed image mosaics. The article does not present the accuracy of the measured distances. The authors also concluded that the SIFT algorithm is robust but takes a long time to calculate. The authors of [2] described many traffic accident scenarios utilizing RPAS. There is, for example, a scenario operating with poor light conditions or darkness. The authors present the possibility of attaching a lighting kit to RPAS. Specifically, the authors used a lighting kit for DJI Phantom 4. The main conclusion is that utilizing RPAS for traffic accident documentation could be more efficient than conventional methods. The authors of [6] tested RPAS usage regarding traffic accident documentation in extreme weather conditions in Kuwait, including high and low temperatures, dusty wind gusts, rain, and low light with good results. They also mention the creation of a 3D model of vehicles. The authors of [7] presented the creation of 2D and 3D models of the traffic accident site. They also used RPAS for large-scale traffic accident (e.g., aircraft accident) documentation [8]. Multi-rotor RPAS were used in all mentioned studies, and the usability of this data was confirmed. However, there is still room for improvement in the speed of acquiring data, the accuracy of the model, and the simplification of the whole process. This study presents the proof of concept and attempts to fill this gap. When a traffic accident happens, to restore the flow of road traffic, three main steps follow. The first step is to save lives, which is indeed the highest priority. The next step is to document the traffic accident site. The third step is to remove vehicles, cargo, debris, etc., and to reopen the highway or road. Regarding time consumption, the first and third steps are highly dependent on traffic accident locations and the integrated rescue system (IRS), or car rescue service driving distance.