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In statistical modeling, cross-validation refers to the practice of fitting a model with part of the available data, and then using predictions of the unused data to test and improve the fitted model. In accident reconstruction, cross-validation is possible when two different measurements can be used to estimate the same accident feature, such as when measured skidmark length and pedestrian throw distance each provide an estimate of impact speed. In this case a Bayesian cross-validation can be carried out by (1) using one measurement and Bayes theorem to compute a posterior distribution for the impact speed, (2) using this posterior distribution to compute a predictive distribution for the second measurement, and then (3) comparing the actual second measurement to this predictive distribution. An actual measurement falling in an extreme tail of the predictive distribution suggests a weakness in the assumptions governing the reconstruction.

The serious highway accidents related to heavy-duty trucks were caused mainly by absent-minded conditions of drivers, according to an investigation of highway accidents in Japan. Thus, a collision avoidance warning system has been developed. A laser radar sensor detects the distance to a reflector of the target vehicle. Together with information of the own vehicle speed detected by a magnetic pick-up, a microcomputer assesses the risk of a rear-end collision and provides warning when a dangerous condition has developed. Warning suppresion is considered in unnecessary situations, such as driving in curves, driving at constant speed and distance, and driving at low speed. The system was installed on a heavy-duty truck, and a system evaluation test was carried out on proving grounds and highways. As the result, false alarms created by reflectable objects located adjacent to the road and unnecessary warning could be sufficiently suppressed, and the system was found to be useful.

The appropriateness of the set of eight frontal stiffness coefficients used by the CRASH3 program to estimate vehicle deformation energy (and to subsequently derive estimates of vehicle delta-V) is examined. This examination consists of constructing so-called CRASH energy plots based on 402 frontal fixed barrier impact tests contained in the NHTSA's Vehicle Test Center Data Base (VTCDB) digital tape file. It is concluded that the use of category coefficients within the CRASH3 program can result in large delta-V errors, reaffirming the inappropriateness of this program for use in individual accident reconstructions. The use of the CRASH3 category stiffness coefficients is seen to generally overestimate vehicle energy absorption for vehicles with small amounts of frontal crush and to underestimate vehicle energy absorption for vehicles sustaining large crush.

The discernment of whether a tire mark on a roadway is the result of a skidding tire or is the result of the rotation of the vehicle with unlocked wheels is important in vehicular accident reconstruction analysis. Resolution of the tire marks left by a vehicle after skidding and/or yawing on dry asphalt were experimentally studied for their similarities and differences under controlled test conditions. This paper analyses the results of this study and shows pictorially the differences for use by the accident reconstructionist. Analytical discussion are also presented that illustrate speed determination as estimated from yaw markings on the roadway.

Vehicle traffic accidents have been extensively studied in various countries, but any differences in traffic accidents the studied areas have not yet been adequately investigated. This paper aims to make a comparison study of head injury risks and kinematics of adult pedestrian accidents in Changsha, China, and Hannover, Germany, as well as correlate calculated physical parameters with injuries observed in real-world accidents of the two cities. A total of 20 passenger cars versus adult pedestrian accidents were collected from the two areas of study, including 10 cases from Changsha and 10 cases from Hannover. Virtual accident reconstructions using PC-Crash and MADYMO software were performed. The in-depth study focused on head injury risks while kinematics were conducted using statistical approaches. The results of the analysis of the Chinese data were compared with those of the German data.

The results of an empirical study comparing the deceleration performance characteristics of several passenger cars is presented. Three definitions of average coefficient of friction are developed and utilized as a basis for comparison of the performance of the vehicles. In addition, the percent energy dissipation before skid mark initiation for each vehicle is also presented and compared. The interpretation of skid marks utilizing a site specific skid test with a dissimilar vehicle is then discussed from the perspective of accident reconstruction.

Light detection ranging (LiDAR) is commonly used to make high-resolution maps by using ultraviolet, visible, or near-infrared light to image objects. It can target a wide range of materials, with many applications, such as in surveying and accident reconstruction. LiDAR-like systems combine laser-focused imaging with the ability to calculate distances by measuring the time for a signal to return using various electronic sensors. LiDAR data capturing has been conducted and verified from many types of equipment manufacturers, however, little research has compared the FARO Terrestrial Laser Scanner and the LiDAR sensor of an iPad Pro. This study compares these two types of equipment addressing ease-of-use, effectiveness, and cost; where the Terrestrial Laser Scanner will be the control for this study. A statistical evaluation was performed of LiDAR data acquired from nine damaged vehicles and one undamaged vehicle.

For several staged collisions, results obtained with closed form reconstruction calculations and with a computerized step-by-step procedure are compared with measured responses. A refined, closed-form reconstruction procedure is defined, derivations of the analytical relationships are outlined and detailed results of sample applications are presented. Closed form calculation procedures for estimating impact conditions became a topic of interest in relation to the development of an automatic starting routine for iterative applications of the Simulation Model of Automobile Collisions (SMAC) computer program. The accuracy of initial estimates of speeds determines the total number of iterative adjustments of SMAC that are required to achieve an acceptable overall match of the evidence. Since a high degree of success was achieved in the refinement of such calculation procedures, the end product, by itself, is considered to be a valuable aid to accident investigations.

In order to evaluate the THOR-50M as a front impact Anthropomorphic Test Device (ATD) for vehicle safety design, the ATD was compared to the H3-50M in matching vehicle crash tests for 20 unique vehicle models from 2 vehicle manufacturers. For the belted driver condition, a total of fifty-four crash tests were investigated in the 56.3 km/h (35 mph) front rigid barrier impact condition. Four more tests were compared for the unbelted driver and right front passenger at 40.2 km/h (25 mph) in the flat frontal and 30-degree right oblique rigid barrier impact conditions. The two ATDs were also evaluated for their ability to predict injury risk by comparing their fleet average injury risk to Crash Investigation Sampling System (CISS) accident data for similar conditions. The differences in seating position and their effect on ATD responses were also investigated.

This paper presents a comprehensive literature review of original equipment event data recorders (EDR) installed in passenger vehicles, as well as a summary of results from the instrumented validation studies. The authors compiled 187 peer-reviewed studies, textbooks, legal opinions, governmental rulemaking policies, industry publications and presentations pertaining to event data recorders. Of the 187 total references, there were 64 that contained testing data. The authors conducted a validation analysis using data from 27 papers that presented both the EDR and corresponding independent instrumentation values for: Vehicle velocity change (ΔV) Pre-Crash vehicle speed The combined results from these studies highlight unique observations of EDR system testing and demonstrate the observed performance of original equipment event data recorders in passenger vehicles.

Designing a vehicle for superior crash safety performance in consumer rating tests such as US-NCAP is a compelling target in the design of passenger vehicles. In today’s context, there is also a high emphasis on making a vehicle as lightweight as possible which calls for an efficient design. In modern vehicle design, these objectives can only be achieved through Computer-Aided Engineering (CAE) for which a detailed CAD (Computer-Aided Design) model of a vehicle is a pre-requisite. In the absence of the latter (i.e. a matured CAD model) at the initial and perhaps the most crucial phase of vehicle body design, a rational approach to design would be to resort to a knowledge-based methodology which can enable crash safety assessment of an assumed design using artificial intelligence techniques such as neural networks.

The reproduction of the vehicle motion is a crucial element of accident reconstruction. Apart from the position of the center of gravity in an inertial coordinate system, the vehicle heading plays an important role. The heading is the sum of the yaw angle and the vehicle body side slip angle. In standard vehicles, the yaw angle can be determined using the yaw rate sensor and the wheel speeds. However, the yaw rate sensor is often subject to temperature drift. The wheel speed signals are forged at low speeds or due to slip. These errors result in significant deviations of reconstructed and real vehicle heading. Therefore, an intelligent combination of these signals is required. This paper describes a fuzzy system which is capable to increase the accuracy of yaw angle calculation by means of fuzzy logic. Before the data is applied to the fuzzy system, it is preprocessed to ensure the accuracy of the fuzzy system inputs.

Flight accidents with modern aircraft are often a result of complex dynamics of the “pilot (automaton1) - vehicle - operational environment” system. When a “critical mass” of the system’s complexity exceeds a certain level, a “chain reaction” of irreversible cause-and-effect links can be spontaneously triggered in the system behavior leading to a catastrophe. An affordable, practically tested technique is proposed to complement current methods of flight accident analysis. A generic situational model of the system behavior and a computer are employed as a virtual test article. This model includes a six-degree-of-freedom non-linear flight dynamics model, a generic situational pilot model (“silicon pilot”), models of anticipated operational factors (conditions), and a tool for flight scenario planning. Available flight recorder data are used to tune the model and reconstruct the accident.

A and B stiffness coefficients to model the frontal stiffness of vehicles is a commonly used and accepted technique within the field of collision reconstruction. Methods for calculating stiffness coefficients rely upon examining the residual crush of a vehicle involved in a crash test. When vehicles are involved in a collision, portions of the crushed vehicle structures rebound from their maximum dynamic crush position. Once the vehicle structures have finished rebounding, the remaining damage is called the residual crush. A problem can arise when the plastic bumper cover rebounds more than the vehicle's structural components, resulting in an air gap between the structural components and the plastic bumper cover. Most modern New Car Assessment Program (NCAP) tests quantify crush in the test reports based on the deformed location of the plastic bumper cover and not the structural components behind the plastic bumper cover. This results in an underreporting of the actual residual crush.

The CRASH3 computer program increasingly is being used by engineers as a tool to reconstruct automobile accidents. The damage analysis portion of CRASH3 provides a useful means for quantifying the change of velocity, ΔV, that was experienced by a vehicle during the collision phase of a traffic accident. The degree of usefulness of the damage analysis portion of the program, however, is dependent upon the availability of valid crush stiffness coefficients. Published crush stiffness coefficients are available for a large number of vehicles *[1] & [2]. These publications, however, contain only a limited number of coefficients that describe the stiffness characteristics of the side structure of vehicles. Engineers are often asked to perform an accident reconstruction when there are neither published stiffness coefficients for the side structure of an involved vehicle nor crash test data from which to determine the stiffness.

Mapping a light source, any light source, is of broad interest to accident reconstructionists, human factors professionals and lighting experts. Such mappings are useful for a variety of purposes, including determining the effectiveness and appropriateness of lighting installations, and performing visibility analyses for accident case studies. Currently, mapping a light source can be achieved with several different methods. One such method is to use an illuminance meter and physically measure each point of interest on the roadway. Another method utilizes a goniometer to measure the luminous intensity distribution, this is a near-field measurement. Both methods require significant time and the goniometric method requires extensive equipment in a lab. A third method measures illumination distribution in the far-field using a colorimeter or photometer.

The accuracy of an accident reconstruction, which employs the damage analysis feature of the CRASH3 computer program, is directly related to the accuracy of the crush stiffness coefficients employed. Crush stiffness coefficients, however, are available only through a limited number of publications and for a limited number of vehicles. In addition, assumptions made in the determination of these published stiffness coefficients bring their accuracy into question and, as a result, limit their value to a reconstructing engineer. It is concluded, therefore, that an engineer must use a critical eye when viewing the results of a CRASH3 reconstruction in which these stiffness coefficients were employed. A method is set forth for quantifying stiffness coefficients from crash test data available in a database which can be obtained from the National Highway Traffic Safety Administration (NHTSA).

Mapping the luminance values of a visual scene is of broad interest to accident reconstructionists, human factors professionals, and lighting experts. Such mappings are useful for a variety of purposes, including determining the effectiveness and appropriateness of lighting installations, and performing visibility analyses for accident case studies. Previous work has shown that pixel intensity captured by consumer-grade digital still cameras can be calibrated to estimate luminance [1-7]. Taking a digital still image and converting this image into a luminance map even further reduces the time required for luminance measurement. Suway and Suway previously presented a methodology for estimating luminance from digital images and video of a scene [1]. In this paper, the authors update this methodology for calculating luminance from a digital camera.

Engineers frequently are faced with reconstructing vehicular accidents based on limited information, i.e. vehicles have been destroyed, witnesses are lost, etc. Accident scene photographs are often the key independent record of what occurred in an accident. One photogrammetry method engineers use to reconstruct accidents is camera reverse projection. This paper presents an extension of camera reverse projection that can be used for accidents where neither the accident scene nor the vehicles are accessible. The technique involves creating models of the vehicles and the scene which can then be used to obtain measurements for reconstructing an accident. The technique is described in detail and a case study is used to illustrate the method. The primary benefits of the method include flexibility in use, intuitive approach, and assistance in sorting out complex vehicular dynamics.

During recent years the accident simulation program PC-Crash was developed. This software simulates vehicle movement before, during and after the impact, using 3D vehicle and scene models. When reconstructing car accidents, quite often questions arise regarding occupant movement and loading. Especially important is the influence of different types of restraint systems on the occupant. MADYMO® is a software tool which was developed by TNO in the Netherlands and which is well known in the automotive industry for the simulation of occupant movement. It allows the simulation of all kinds of modern restraint systems such as airbags and seatbelts with and without pretensioners. As the software is used in the automotive industry quite extensively, a huge validated database of dummy and human models is available. Since MADYMO® demands the setup of quite complicated input files, its use normally requires a high level of expertise.