One of the primary objectives of the National Highway Traffic Safety Administration (NHTSA) is to reduce the staggering human toll and property damage that motor vehicle traffic crashes impose on our society. Crashes each year result in thousands of lives lost, hundreds of thousands of injured victims, and billions of dollars in property damage. Accurate data are required to support the development, implementation, and assessment of highway safety programs aimed at reducing this toll. This multi-year analytical user's manual provides documentation on variables that are contained in the GES and other useful information that will enable the users to become familiar the data system.

The National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) is a nationwide crash data collection program sponsored by the U.S. Department of Transportation. It is operated by the National Center for Statistics and Analysis (NCSA) of the National Highway Traffic Safety Administration (NHTSA). The NASS CDS provides an automated, comprehensive national traffic crash database. Data collection began in 1979 in 10 geographic sites, called Primary Sampling Units (PSU's). In 2002, three additional PSUs were added to the system so that the 2004 NASS CDS file contains data from 27 PSU's. These data are weighted to represent all police reported motor vehicle crashes occurring in the USA during the year involving passenger cars, light trucks and vans that were towed due to damage. The NASS program was re-evaluated in the mid-1980's. This re-evaluation resulted in changes, which were implemented by NHTSA in January 1988. NASS now has two major operating components: (1) the General Estimates System (GES) which collects data on a sample of police traffic crash reports; and (2) the Crashworthiness Data System (CDS) which collects additional detailed information on a sample of police reported traffic crashes.

This report identifies crash imminent test scenarios based on common pre-crash scenarios for integrated vehicle-based safety systems that alert the driver of a light vehicle or a heavy truck to an impending rear-end, lane change, or run-off-road crash. Pre-crash scenarios describe vehicle movements and critical events immediately prior to the crash. The General Estimates System (GES) crash database was queried to distinguish common pre-crash scenarios for light vehicles (2003 GES) and heavy trucks (2000-2003 GES) in terms of their frequency of occurrence. Analysis of two-vehicle rear-end crashes revealed four dominant scenarios that accounted for 97 percent of light-vehicle crashes and 95 percent of heavy-truck crashes in which the subject vehicle was striking. Four scenarios were also identified from an analysis of two-vehicle lane change crashes, comprising 65 percent of light-vehicle crashes and 76 percent of heavy-truck crashes in which the subject vehicle was encroaching onto another vehicle in adjacent lanes. There were five single-vehicle, run-off-road scenarios representing 63 percent of light-vehicle crashes and 83 percent of heavy-truck crashes, excluding crashes caused by vehicle failure or evasive maneuver. An additional set of scenarios is proposed to address multiple threats from near simultaneous critical events. This report also provides a statistical description of individual scenarios in terms of their environmental factors, roadway geometry, and speed conditions.

This book provides a state-of-the-art look at the applied biomechanics of accidental injury and prevention. The editors, Drs. Narayan Yoganandan, Alan M. Nahum and John W. Melvin are recognized international leaders and researchers in injury biomechanics, prevention and trauma medicine. They have assembled renowned researchers as authors for 29 chapters to cover individual aspects of human injury assessment and prevention. This third edition is thoroughly revised and expanded with new chapters in different fields. Topics covered address automotive, aviation, military and other environments. Field data collection; injury coding/scaling; injury epidemiology; mechanisms of injury; human tolerance to injury; simulations using experimental, complex computational models (finite element modeling) and statistical processes; anthropomorphic test device design, development and validation for crashworthiness applications in topics cited above; and current regulations are covered. Risk functions and injury criteria for various body regions are included. Adult and pediatric populations are addressed. The exhaustive list of references in many areas along with the latest developments is valuable to all those involved or intend to pursue this important topic on human injury biomechanics and prevention. The expanded edition will interest a variety of scholars and professionals including physicians, biomedical researchers in many disciplines, basic scientists, attorneys and jurists involved in accidental injury cases and governmental bodies. It is hoped that this book will foster multidisciplinary collaborations by medical and engineering researchers and academicians and practicing physicians for injury assessment and prevention and stimulate more applied research, education and training in the field of accidental-injury causation and prevention.

This report presents the results of an analysis effort undertaken to address the following research question: What sensor(s) can be cost effectively added to vehicles on a wide scale to significantly improve our understanding and modeling of naturalistic near-crash/pre-crash driver performance? Current sensor and computer technology allows for the efficient collection and storage of driver and vehicle performance data on board vehicles. Crash data recorders or black boxes exist today on many vehicles though they are limited in number of recorded parameters and storage capacity. However, their capability is increasing. Recent field operational tests of advanced-technology crash avoidance systems and naturalistic driving data collection efforts have employed comprehensive data acquisition systems to characterize driver and vehicle performance as well as the driving environment. These projects gathered data on driver exposure to various environmental factors and on driver encounters with driving conflicts, near-crashes, and actual crashes. Unfortunately, the in-vehicle data acquisition packages in these projects cost over $10,000 per vehicle. It would be advantageous to build and install a very small, inexpensive package under $1,000 in a vehicle fleet of 5,000 or more. The presence of low-cost near-crash/crash event data recorders (EDRs) on thousands of vehicles would enable a more accurate assessment of safety benefits for intelligent vehicle crash avoidance technologies, and would greatly improve the quality of data in national crash databases such as the National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) and General Estimates System (GES).