Methods for describing different results obtained from different methods in accident reconstruction

Mapping the knowledge of traffic collision Reconstruction: A scientometric analysis in CiteSpace, VOSviewer, and SciMAT

Traffic collisions are incidents with high fatality rate which generate billions of US dollars of loss worldwide each year. Post-collision scene reconstruction, which involves knowledge of multiple disciplines, is an important approach to restore the traffic collision and infer the cause of it. This paper uses software CiteSpace, VOSviewer, and SciMAT to conduct a visualization study of knowledge mapping on the literature of traffic collision scene reconstruction from 2001 to 2021 based on the Web of Science database. Knowledge mapping is a cutting-edge research method in scientometric, which has been widely applied in medicine and informatics. Compared with traditional literature review, knowledge mapping with visual techniques identifies hot keywords and key literature in the field more scientifically, and displays them in schematic diagrams intuitively which allows to further predict potential hotspots. A total of 803 original papers are retrieved to analyze and discuss the evolution of the field in the past 20 years, from macro to micro, in term of background information, popular themes, and knowledge structure. Results indicate the number of publications in this field is limited, and collaborations among authors and among institutions are insufficient. In the meantime, mappings imply the top three hot themes being scene reconstruction, computer technology, and injuries. The introduction of AI related technologies, such as neural networks and genetic algorithms, into collision reconstruction would be a potential research direction.

A precise analytic model for simulating tire's yaw marks striations

Tire's yaw marks are characterized by oblique striations which contain important information about the braking and steering maneuvers performed by drivers in road accidents. This paper introduces an original method for realistic numerical and graphic simulation of yaw marks striations (scuffs) for the purpose of accidents reconstruction and analysis. The introduced method includes authors' implementation of an existing 3D vehicle dynamics model using a non-rigid wheel/ground interaction model. Results are compared to commercial accident reconstruction software concerning vehicle dynamics implementation, and then examples of lateral sliding situations are discussed to illustrate the application of the proposed yaw marks striations model in road accidents reconstruction.

A new approach for uncertainty analysis of ETW Rider Head Injury Reconstruction via Coupling Response Surface and Monte Carlo methodologies

Traditional vehicle accident reconstructions do not take into account all existing uncertainties and may over- or under-estimate the injury risk. The objective of this study was to introduce a new uncertainty analysis method by applying Response Surface-Monte Carlo Methods (RS-MCM) to predict head injury risk in real electric two wheelers (ETW) to vehicle accidents. Vehicle impact velocity ranges in three detailed ETWs accidents (including video records and injury reports) were estimated using direct linear transformation (DLT) or video frame (VF) methods. A response surface methodology (RSM) was used to obtain an approximate model of the each real ETW accident, and a vehicle impact velocity distribution was estimated by applying the Monte Carlo Method (MCM) to the resulting model. If the velocity distribution was in agreement with the initial estimated velocity, the reconstruction quality was deemed acceptable. The injury severity was then assessed using the initial conditions resulting from the range of potential head impact conditions identified in the reconstruction activities. The identified head linear and angular impact velocities were input to finite element analyses to the THUMS Ver4.02 pedestrian head model and resulting in head injury criteria (HIC). The HIC values were further explored using the same RSM method used earlier to establish impact conditions. The distribution of reconstructed AIS levels show good agreement with the injury results from forensic reports. The results illustrated that the RS-MCM enriches the information for head trauma injury mechanisms caused by the vehicle collisions or ground impact.

Methods for fusing uncertain results obtained from different models in accident reconstruction

Considering that almost all existing solutions of fusing different reconstructed results require experts’ opinions and the issue of how to fuse probabilistic results and mixed results has not been discussed. Two solutions are proposed. The first is based on the Monte Carlo Method (FMCM), while the second is based on the Sub-Interval Technique (FSIT). The method based on FMCM generates sample points according to the distribution of each uncertain result firstly, and then gives out the cumulative distribution function of the final fused result by statistical analysis. The method based on FSIT gets the result fusion interval set according to lower and upper bounds of all interval results and a given length d of each sub-interval firstly, and then calculate the weighted matrix of the result fusion interval. As a result, the cumulative distribution function of the final fused result can also be given out by statistical analysis. Finally, three real accidents are given to demonstrate the methods of FMCM and FSIT, the results of which show that both work well in practice.