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A review was conducted of injuries associated with ejection through motor vehicle glazing, using the 1988 through 1991 National Accident Sampling System data maintained by the National Highway Traffic Safety Administration. The review indicated that one percent of the occupants in towaway crashes were ejected and that 22 percent of fatalities in towaway crashes were ejected. Fifty-three percent of complete ejections were through the glazing openings in motor vehicles. Current motor vehicle glazing does not contribute significantly to occupant injuries, but the effects of glazing changes on serious injuries will need to be considered.

This paper presents the methodology and results of an analysis of the available information on motor vehicle safety which could be used to provide a basis for establishing priorities for future Government and private sector efforts directed at enhanced crash protection. The work was stimulated by several factors: (1) 5 years have elapsed since the National Highway Traffic Safety Administration (NHTSA) published a plan for motor vehicle safety research and development, (2) motor vehicles have changed substantially over the past several years, (3) the quantity and quality of accident data and vehicle crash performance information have increased dramatically over the past 5 years, and (4) Government policies and the amount of Government and private sector resources available for future efforts are changing.

For realistic traffic modeling, real-world traffic calibration data is needed. These data include a representative road network, road users count by type, traffic lights information, infrastructure, etc. In most cases, this data is not readily available due to cost, time, and confidentiality constraints. Some open-source data are accessible and provide this information for specific geographical locations, however, it is often insufficient for realistic calibration. Moreover, the publicly available data may have errors, for example, the Open Street Maps (OSM) does not always correlate with physical roads. The scarcity, incompleteness, and inaccuracies of the data pose challenges to the realistic calibration of traffic models. Hence, in this study, we propose an approach based on spatial interpolation for addressing sparsity in vehicle count data that can augment existing data to make traffic model calibrations more accurate.

With the emergence of Driving Automation Systems (SAE levels 1-5), the necessity arises for methods of evaluating these systems. However, these systems are much more challenging to evaluate than traditional safety features (SAE level 0). This is because an understanding of the Driving Automation system’s response in all possible scenarios is desired, but prohibitive to comprehensively test. Hence, this paper attempts to evaluate one such system, by modeling its behavior. The model generated parameters not only allow for objective comparison between vehicles, but also provide a more complete understanding of the system. The model can also be used to extrapolate results by simulating other scenarios without the need for conducting more tests. In this paper, low speed automated driving (also known as Traffic Jam Assist (TJA)) is studied. This study focused on the longitudinal behavior of automated vehicles while following a lead vehicle (LV) in traffic jam scenarios.

The computer program, CRASH 3, uses the equations of motion to estimate the changes in velocity of motor vehicles in crashes and their trajectories following a collision. It was developed in the mid-1970's by McHenry at Caispan for use in accident research. There are important limitations on where and how it should be used. CRASH 3 requires a skilled reconstruction of a crash and an interactive execution of the program to provide reasonably accurate results. The paper also discusses the sensitivity of CRASH 3 to various parameters and the potential for improving it. This paper presents the views of its author and not necessarily those of the National Highway Traffic Safety Administration (NHTSA).

This paper focuses on the overall structure of the Crash Injury Research and Engineering Network (CIREN), how data are collected, and what makes it unique. It discusses how it can be used to expand and enhance the information in other databases. CIREN is a collaborative effort to conduct research on crashes and injuries at nine Level 1 Trauma Centers which are linked by a computer network. Researchers can review data and share expertise, which will lead to a better understanding of crash injury mechanisms and the design of safer vehicles. CIREN data are being used in outreach and education programs on motor vehicle safety. CIREN outreach and education has already been credited with lifesaving information dissemination.

Connected vehicle (CV) technology is among the most heavily researched areas in both the academia and industry. The vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to pedestrian (V2P) communication capabilities enable critical situational awareness. In some cases, these vehicle communication safety capabilities can overcome the shortcomings of other sensor safety capabilities because of external conditions such as 'No Line of Sight' (NLOS) or very harsh weather conditions. Connected vehicles will help cities and states reduce traffic congestion, improve fuel efficiency and improve the safety of the vehicles and pedestrians. On the road, cars will be able to communicate with one another, automatically transmitting data such as speed, position, and direction, and send alerts to each other if a crash seems imminent. The main focus of this paper is the implementation of Cooperative Collision Avoidance (CCA) for connected vehicles.

Intersections have a high risk of vehicle-to-vehicle conflicts because of the overlapping traffic flow from multiple roads. To understand the factors contributing to the crashes, this study examines the common characteristics in intersection-related crash and near- crash events, such as the existence of traffic control devices, the driver at fault, and occurrence of visual obstructions. The descriptive data of the crash and near-crash events recorded in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) database is used in categorization and statistical analysis in this study. First, the events are divided into seven categories based on trajectories of the conflicting vehicles. The categorization provides the basis for in-depth analysis of crash-contributing factors in specific confliction patterns. Subsequently, descriptive statistics are used to portray each of the categories.

A major focus of the National Highway Traffic Safety Administration's (NHTSA) vehicle compatibility and aggressivity research program is the development of a laboratory test procedure to evaluate compatibility. This paper is written to explain the associated goals, issues, and design considerations and to review the preliminary results from this ongoing research program. One of NHTSA's activities supporting the development of a test procedure involves investigating the use of an mobile deformable barrier (MDB) into vehicle test to evaluate both the self-protection (crashworthiness) and the partner-protection (compatibility) of the subject vehicle. For this development, the MDB is intended to represent the median or expected crash partner. This representiveness includes such vehicle characteristics as weight, size, and frontal stiffness. This paper presents distributions of vehicle measurements based on 1996 fleet registration data.

With the advancement of the autonomous vehicle development, the different possibilities of improving fuel economy have increased significantly by changing the driver or powertrain response under different traffic conditions. Development of new fuel-efficient driving strategies requires extensive experiments and simulations in traffic. In this paper, a fuel efficiency simulator environment with existing simulator software such as Simulink, Vissim, Sumo, and CarSim is developed in order to reduce the overall effort required for developing new fuel-efficient algorithms. The simulation environment is created by combining a mid-sized sedan MATLAB-Simulink powertrain model with a realistic microscopic traffic simulation program. To simulate the traffic realistically, real roads from urban and highway sections are modeled in the simulator with different traffic densities.

In the last decade, the accelerated advancements in manufacturing techniques and material science enabled the automotive industry to manufacture commercial vehicles at more affordable rates. This, however, brought about roadways having to accommodate an ever-increasing number of vehicles every day. However, some roadways, during specific hours of the day, had already been on the brink of reaching their capacity to withstand the number of vehicles travelling on them. Hence, overcrowded roadways create slow traffic, and sometimes, bottlenecks. In this paper, a Dynamic Speed Harmonization (DSH) algorithm that regulates the speed of a vehicle to prevent it from being affected by bottlenecks has been presented. First, co-simulations were run between MATLAB Simulink and CarSim to test different deceleration profiles.

Energy management plays a key role in achieving higher fuel economy for plug-in hybrid electric vehicle (PHEV) technology; the state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining the fuel consumed. The energy management algorithm should be designed to meet all driving scenarios while achieving the best possible fuel economy. The knowledge of the power requirement during a driving trip is necessary to achieve the best fuel economy results; performance of the energy management algorithm is closely related to the amount of information available in the form of road grade, velocity profiles, trip distance, weather characteristics and other exogenous factors. Intelligent transportation systems (ITS) allow vehicles to communicate with one another and the infrastructure to collect data about surrounding, and forecast the expected events, e.g., traffic condition, turns, road grade, and weather forecast.

In the past, research on blockchain technology has addressed security and privacy concerns within intelligent transportation systems for critical V2I and V2V communications that form the backbone of Internet of Vehicles. Within trucking industry, a recent trend has been observed towards the use of blockchain technology for operations. Industry stakeholders are particularly looking forward to refining status quo contract management and vehicle maintenance processes through blockchains. However, the use of blockchain technology for enhancing vehicle performance in fleets, especially while considering the fact that modern-day intelligent vehicles are prone to cyber security threats, is an area that has attracted less attention. In this paper, we demonstrate a case study that makes use of blockchains to securely optimize the fuel economy of fleets that do package pickup and delivery (P&D) in urban areas.

A comprehensive review of casualty-reducing effectiveness estimates of child safety seats in actual use, obtained by statistical analyses of highway accident data. Recent analyses of large samples of New York and Maryland accidents show statistically significant injury reductions for child safety seats; so does a new analysis of the National Highway Traffic Safety Administration's accident files. Results from Washington State, Tennessee, New Jersey, and Idaho are also reviewed, as are Nationwide restraint usage and fatality trends. The findings are critically examined for possible data biases. It is concluded that child safety seats definitely reduce deaths and injuries in highway crashes, but that their effectiveness cannot be accurately estimated at this time because of inconsistencies and possible biases in the various studies.

This paper describes a Field Operational Test (FOT) of an Automated Collision Notification (ACN) System, a new way to provide definitive pre- hospital medical care to people injured in motor vehicle crashes. This FOT is part of the National Highway Traffic Safety Administration's (NHTSA's) research being conducted under the U.S. Department of Transportation's Intelligent Transportation Systems (ITS) program. Management of the program is by the Office of Vehicle Safety Research in NHTSA. The ACN FOT is a demonstration of the application of advanced technology for the improvement of pre-hospital emergency care for motor-vehicle crash victims. The test, involving approximately 850 volunteers' vehicles, was conducted in rural Western New York State. A partnership of local public agencies and private corporations, led by Veridian, Inc., performed the test.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

This paper describes ongoing research conducted by the National Highway Traffic Safety Administration (NHTSA) to evaluate the potential of safety restraints on large school buses. School bus transportation is one of the safest forms of transportation in the United States. Large school buses provide protection because of their visibility, size, and weight, as compared to other types of motor vehicles. Additionally, they are required to meet minimum Federal Motor Vehicle Safety Standards (FMVSS) mandating compartmentalized seating, emergency exits, roof crush and fuel system integrity, and minimum bus body joint strength.

The National Highway Safety Administration (NHTSA) is collecting crash data relating to large trucks. Two data collection programs are specified. One is a crash causation study to investigate the cause of fatal and serious large truck crashes over two years. The other study is a continuous effort collecting data on large truck motor carrier crashes in each state, as coded on police accident reports.

This paper is primarily a printed listing of the National Highway Traffic Safety Administration’s (NHTSA) Light Vehicle Inertial Parameter Database. This database contains measured vehicle inertial parameters from SAE Paper 930897, “Measured Vehicle Inertial Parameters -NHTSA’s Data Through September 1992” (1), as well as parameters obtained by NHTSA since 1992. The proceeding paper contained 414 entries. This paper contains 82 new entries, for a total of 496. The majority of the entries contain complete vehicle inertial parameters, some of the entries contain tilt table results only, and some entries contain both inertia and tilt table results. This paper provides a brief discussion of the accuracy of inertial measurements. Also included are selected graphs of quantities listed in the database for some of the 1998 model year vehicles tested.

This paper presents an overview of work performed by the National Highway Traffic Safety Administration's (NHTSA) Vehicle Research and Test Center (VRTC) to test, validate, and improve the planned National Advanced Driving Simulator's (NADS) vehicle dynamics simulation. This vehicle dynamics simulation, called NADSdyna, was developed by the University of Iowa's Center for Computer-Aided Design (CCAD) NADSdyna is based upon CCAD's general purpose, real-time, multi-body dynamics software, referred to as the Real-Time Recursive Dynamics (RTRD), supplemented by vehicle dynamics specific submodules VRTC has “beta tested” NADSdyna, making certain that the software both works as computer code and that it correctly models vehicle dynamics. This paper gives an overview of VRTC's beta test work with NADSdyna. The paper explains the methodology used by VRTC to validate NADSdyna.