Motorcycle Accident Reconstruction

Accident reconstruction utilizes principles of physics and empirical data to analyze the physical, electronic, video, audio, and testimonial evidence from a crash, to determine how and why the crash occurred, how the crash could have been avoided, or to determine whose description of the crash is most accurate. This process draws together aspects of mathematics, physics, engineering, materials science, human factors, and psychology, and combines analytical models with empirical test data. Different types of crashes produce different types of evidence and call for different analysis methods. Still, the basic philosophical approach of the reconstructionist is the same from crash type to crash type, as are the physical principles that are brought to bear on the analysis. This book covers a basic approach to accident reconstruction, including the underlying physical principles that are used, then details how this approach and the principles are applied when reconstructing motorcycle crashes. This second edition of Motorcycle Accident Reconstruction presents a thorough, systematic, and scientific overview of the available methods for reconstructing motorcycle crashes. This new edition contains: Additional theoretical models, examples, case studies, and test data. An updated bibliography incorporating the newest studies in the field. Expanded coverage of the braking capabilities of motorcyclists. Updated, refined, and expanded discussion of the decelerations of motorcycles sliding on the ground. A thoroughly rewritten and expanded discussion of motorcycle impacts with passenger vehicles. Updated coefficients of restitution for collisions between motorcycles and cars. A new and expanded discussion of using passenger car EDR data in motorcycle accident reconstruction. A new section covering recently published research on post-collision frozen speedometer readings on motorcycles. A new section on motorcycle interactions with potholes, roadway deterioration, and debris and expanded coverage of motorcycle falls. This second edition of Motorcycle Accident Reconstruction is a must-have title for accident reconstructionists, forensic engineers, and all interested in understanding why and how motorcycle crashes occur.

Vehicle Accident Analysis and Reconstruction Methods

In this third edition of Vehicle Accident Analysis & Reconstruction Methods, Raymond M. Brach and R. Matthew Brach have expanded and updated their essential work for professionals in the field of accident reconstruction. Most accidents can be reconstructed effectively using of calculations and investigative and experimental data: the authors present the latest scientific, engineering, and mathematical reconstruction methods, providing a firm scientific foundation for practitioners. Accidents that cannot be reconstructed using the methods in this book are rare. In recent decades, the field of crash reconstruction has been transformed through the use of technology. The advent of event data records (EDRs) on vehicles signaled the era of modern crash reconstruction, which utilizes the same physical evidence that was previously available as well as electronic data that are measured/captured before, during, and after the collision. There is increased demand for more professional and accurate reconstruction as more crash data is available from vehicle sensors. The third edition of this essential work includes a new chapter on the use of EDRs as well as examples using EDR data in accident reconstruction. Early chapters feature foundational material that is necessary for the understanding of vehicle collisions and vehicle motion; later chapters present applications of the methods and include example reconstructions. As a result, Vehicle Accident Analysis & Reconstruction Methods remains the definitive resource in accident reconstruction.

Applications of Matchmoving for Forensic Video Analysis of a Fatal Sprint Car Accident: Part I

The methodology used for the reconstruction of a high-profile Sprint Car accident that was captured by at least three different video recording devices is presented in two parts. Part I discusses a classical method of an accident reconstruction, and Part II discusses matchmoving technique to accurately analyze the video footage of the accident. Accidents captured on video are unlike most simple car collision evaluations and require expert knowledge from experienced professionals. Understanding the race car vehicle dynamics as it relates to recorded video footage allows a proper methodology to be followed in order to gather and process the evidence needed to provide meaningful data to the trier of fact. This paper discusses the classical process to reconstruct the accident as well as the currently acceptable scientific methodologies that were used to collect and interpolate the available scientific evidence. A visualization of the vehicles involved, Sprint Car #13 (SC#13) and Sprint Car #14 (SC#14), is shown in Figure 1.

A Machine Learning Enhanced Mechanistic Simulation Framework for Functional Deficit Prediction in TBI

Resting state functional magnetic resonance imaging (rsfMRI), and the underlying brain networks identified with it, have recently appeared as a promising avenue for the evaluation of functional deficits without the need for active patient participation. We hypothesize here that such alteration can be inferred from tissue damage within the network. From an engineering perspective, the numerical prediction of tissue mechanical damage following an impact remains computationally expensive. To this end, we propose a numerical framework aimed at predicting resting state network disruption for an arbitrary head impact, as described by the head velocity, location and angle of impact, and impactor shape. The proposed method uses a library of precalculated cases leveraged by a machine learning layer for efficient and quick prediction. The accuracy of the machine learning layer is illustrated with a dummy fall case, where the machine learning prediction is shown to closely match the full simulation results. The resulting framework is finally tested against the rsfMRI data of nine TBI patients scanned within 24 h of injury, for which paramedical information was used to reconstruct in silico the accident. While more clinical data are required for full validation, this approach opens the door to (i) on-the-fly prediction of rsfMRI alterations, readily measurable on clinical premises from paramedical data, and (ii) reverse-engineered accident reconstruction through rsfMRI measurements.

PERSONALIZED CUSTOMIZATION METHOD OF HYBRID HUMAN MODEL FOR PEDESTRIAN-VEHICLE ACCIDENT RECONSTRUCTION

Traffic accident reconstruction is a reverse dynamic problem, which requires hundreds of iterations to reconstruct the whole process of accident. However, in current pedestrian-vehicle accident reconstructions, it is difficult to quickly establish a pedestrian model based on specific cases, and it is hard to solve the contradiction between calculation accuracy and calculation time. In this paper, a personalized pedestrian customization method is proposed. First, the pedestrian structure is divided into independent modules according to obvious bony markers. For each independent module, multi-body (MB) model and finite element (FE) model are established, respectively. Then the appropriate modules are selected to form the whole hybrid pedestrian model. This method can customize the structure of pedestrian model according to the injury characteristics of pedestrians in specific accidents, and customize the parameters of pedestrian model according to the height and weight of pedestrians. The impact simulation tests are carried out on hybrid pedestrian models to verify the reliability of the models. The proposed method can effectively improve the modeling efficiency of pedestrian models and the reconstruction quality of pedestrian traffic accidents.