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Engine control units (ECUs) on heavy trucks have been capable of storing “last stop” or “hard stop” data for some years. These data provide useful information to accident reconstruction personnel. In past studies, these data have been analyzed and compared to higher-resolution on-vehicle data for several heavy trucks and several makes of passenger cars. Previous published studies have been quite helpful in understanding the limitations and/or anomalies associated with these data. This study was designed and executed to add to the technical understanding of heavy truck event data recorders (EDR), specifically data associated with a modern Cummins power plant ECU. Emergency “full-treadle” stops were performed at many combinations of load-speed-surface coefficient conditions. In addition, brake-in-curve tests were performed on wet Jennite for various conditions of disablement of the braking system.

This research paper builds onto the wealth of technical information that has been published in the past by engineers such as Flick, Radlinski, and Heusser. For this paper, the pushrod force versus chamber pressure data published by Heusser are supplemented with data taken from brake chamber types not reported on by Heusser in 1991. The utility of Heusser's braking force relationships is explored and discussed. Finally, a straightforward and robust method for calculating truck braking performance, based on the brake stroke measurements and published heavy truck braking test results, is introduced and compared to full-scale vehicle test data.

This paper is a follow up to a study published in 2005 1 on the same topic and presents a study that was conducted to compare vehicle speed change and acceleration data from full-scale testing to results generated by computer simulation using the SImulation MOdel Non-linear (SIMON) vehicle dynamic simulation model version 3.1 within the Human Vehicle Environment (HVE) software version 5.2. SIMON will be referred to in this paper as the computer or simulation model, while HVE will be referred to as the computer software. In the previous study, a simple method to model the curb was developed and version 2.0 of the simulation model was validated, for delta-v, up to approximately 6.7 m/s (15 mph) and for vertical accelerations, up to speeds of approximately 4.5 m/s (10 mph).

Investigators of vehicular incidents often seek to recover data stored within on-board computer systems. For commercial vehicles, the primary source for this information is the engine control module (ECM). The data stored in these modules, not unlike passenger vehicles, varies widely among manufacturers, as do the hardware and software required to recover such data. Further, the options, and associated risks, involved with attempting to recover this data has a similarly wide variance relative to the engine manufacturer, incident related circumstances, and the tools currently available to perform such downloads. There are two primary paths available to obtain this data: (1) via the vehicle data bus (e.g. SAE J1939 or J1708 ) or (2) direct-to-module (DTM) connection. When using the DTM method, power is applied to an ECM, and the module measures the various engine control and monitoring components for validity.

The shape of an acceleration pulse in an impact is not only affected by the change in velocity, but also by the geometry and stiffness of the both the striking vehicle and the struck object. In this paper, the frontal crash performance of a full-size pickup is studied through a series of impact tests with a rigid pole and with a flat barrier. Each rigid pole test is conducted at one of four locations across the front of the vehicle and at impact speeds of 10 mph, 20 mph, or 30 mph. The flat barrier tests are conducted at 10 mph, 15 mph, 20 mph, and 30 mph. The vehicle crush and acceleration pulses resulting from the pole tests are compared to those resulting from the barrier tests. The severity of pole impacts and the severity of flat barrier impacts are compared based on peak accelerations and pulse durations of the occupant compartment.

Accident reconstructionists use several different approaches to determine vehicle equivalent impact speed from damage due to narrow object impacts. One method that is used relates maximum crush to equivalent impact speed with a bilinear curve. In the past, this model has been applied to several passenger cars with unibody construction. In this paper, the approach is applied to a body-on-frame vehicle. Several vehicle-to-rigid pole impact tests have been conducted on a full-size pickup at different speeds and impact locations: centrally located across the vehicle's front and outside the frame rail. A bilinear model relating vehicle equivalent impact speed to maximum crush is developed for the impact locations. These results are then compared to results obtained from other body-on-frame vehicles as well as unibody vehicles. Other tests such as impacts on the frame rail and barrier impacts are also presented. Limitations to this bilinear approach are discussed.

This paper presents the results of an in-depth study of the measurement of occupant kinematic response on the S-E-A Roll Simulator. This roll simulator was built to provide an accurate and repeatable test procedure for the evaluation of occupant protection and restraint systems during roll events within a variety of occupant compartments. In the present work this roll simulator was utilized for minimum-energy, or threshold type, rollover events of recreational off-highway vehicles (ROVs). Input profiles for these tests were obtained through a separate study involving autonomous full vehicle tests [1]. During simulated roll events anthropomorphic test device (ATD) responses were measured using on-board high speed video, an optical three-dimensional motion capture system (OCMS) and an array of string potentiometers.

According to NHTSA's 2011 Traffic Safety Facts [1], passenger vehicle occupant fatalities continued the strong decline that has been occurring recently. In 2011, there were 21,253 passenger vehicles fatalities compared to 22,273 in 2010, and that was a 4.6% decrease. However; large-truck occupant fatalities increased from 530 in 2010 to 635 in 2011, which is a 20% increase. This was a second consecutive year in which large truck fatalities have increased (9% increase from 2009 to 2010). There was also a 15% increase in large truck occupant injuries from 2010. Moreover, the fatal crashes involving large trucks increased by 1.9%, in contrast to other-vehicle-occupant fatalities that declined by 3.6% from 2010. The 2010 accident statistics NHTSA's report reveals that large trucks have a fatal accident involvement rate of 1.22 vehicles per 100 million vehicle miles traveled compared to 1.53 for light trucks and 1.18 for passenger cars.

This paper describes the design and development of the hardware, electronics, and software components of a state-of-the-art automated steering controller, the SEA, Ltd. ASC. The function of the ASC is to input to a vehicle virtually any steering profile with both high accuracy and repeatability. The ASC is designed to input profiles having steering rates and timing that are in excess of the limits of a human driver. The ASC software allows the user to specify steering profiles and select controller settings, including motor controller gains, through user-interface windows. This makes it possible for the test driver to change steering profiles and settings immediately after running any test maneuver. The motor controller used in the ASC offers self-contained signal input, output, and data storage capabilities. Thus, the ASC can operate as a standalone steering machine or it can be incorporated into typical existing, on-vehicle data acquisition systems.

Markings or observable anomalies on vehicle seat belt restraint systems can be classified into two categories: (1) Those caused by collision forces, or “loading marks” and (2) those created by noncollision situations, or “normal usage marks” [1]. A survey was conducted of both crash tested and non-crash tested vehicles in order to collect data on both categories of markings. This paper examines and analyzes the markings caused by both collision and noncollision load scenarios in order to illustrate and evaluate their unique differences as well as provide a general pattern of severity relative to different loading conditions.

In the field of accident reconstruction, there has been a significant amount of effort devoted to the calculation and derivation of vehicle crush energy and vehicle stiffness. Crush energy is usually calculated with a crush profile and crush stiffness. But, oftentimes, crush profiles and/or crush stiffnesses are not available and accident constructionists face the situation of insufficient information. In some such cases, the force balance method can be used to reduce the uncertainty. The method follows from Newton's Third Law, i.e., the impact force exerted on one vehicle is balanced by the force exerted on the other vehicle. With the help of this method, crush profile or crush stiffness can be derived. As a result, the crush energy can then be calculated with improved accuracy. This ultimately increases the accuracy of the overall accident reconstruction. In this paper, examples will be given to illustrate the use of such a methodology.

A single-vehicle crash involving an SUV led to the study of the failure of the anti-sway bar linkage and tire pressure and their relative effects on the handling characteristics of the vehicle. The SUV, having been involved in a rollover, was found with the anti-sway bar drop link disconnected from the suspension lower A-arm assembly. Also, after the crash, the tire pressure in the front tires on the subject vehicle was measured to be above the value specified by the SUV manufacturer; however, the pressure for one of the rear tires was measured to be roughly half of the SUV manufacturer’s recommended pressure. The other rear tire was deflated. The testing described herein addresses the question of what effects the anti-sway bar drop link disconnection or reduced rear axle tire pressure would have on the SUV’s pre-accident handling and driveability.

Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

There have been many studies regarding the stability of vehicles following a sudden air loss event in a tire. Previous works have included literature reviews, full-scale vehicle testing, and computer modeling analyses. Some works have validated physics-based computer vehicle simulation models for passenger vehicles and other works have validated models for heavy commercial vehicles. This work describes a study wherein a validated vehicle dynamics computer model has been applied to extrapolate results to higher event speeds that are consistent with travel speeds on contemporary North American highways. This work applies previously validated vehicle dynamics models to study the stability of a five-axle commercial tractor-semitrailer vehicle following a sudden air loss event for a steer axle tire. Further, the work endeavors to understand the analytical tire model for tires that experience a sudden air loss.

Determining impact speeds is an important factor in any accident reconstruction. Event data recorders are now commonplace in on-road vehicles and provide an added tool for the accident reconstructionist. However, in low-speed collisions where impact severity is often important, event data recorders fail to record data as the minimum threshold for impact severity sometimes is not met. Alternatively, damage-based methods may be ineffective in quantifying the severity of the impact due to a lack of defined vehicle crush damage. These types of scenarios oftentimes present themselves as a bullet vehicle in the beginning processes of accelerating from a stop or when a stopped target vehicle is rear-ended from behind by the bullet vehicle.

Delta-V and Barrier Equivalent Velocity (BEV) are terms that have been used for many years to describe aspects of what happened to a vehicle when an impact occurred. That is, they are used to describe some physical change in the vehicle state before the impact as compared to after the impact. Specifically, the Delta-V describes the change in the vehicle velocity vector from just before the impact until just after the impact. The BEV attempts to quantify the energy required to cause the damage associated with an impact. In order to understand what happens to a vehicle and its occupants during an impact, it is necessary to examine the acceleration pulse undergone by the vehicle during the impact. The acceleration pulse describes, in detail, how the Delta-V occurs as a function of time, and is related with the deformation of the vehicle as well as the object contacted by the vehicle during an impact.

Accident reconstruction specialists have long relied on post-crash deformation and energy equivalence calculations to determine impact severity and the experienced change in velocity during the impact event. In order to utilize post-crash deformation, information must be known about the vehicle's structure and its ability to absorb crash energy. The Federal Motor Vehicle Safety Standards (FMVSS), the New Car Assessment Program (NCAP), and the Insurance Institute of Highway Safety (IIHS), have created databases with crash testing data for a wide range of vehicles. These crash tests allow reconstruction specialists to determine a specific vehicle's ability to absorb energy as well as to generalize the energy absorption characteristics across vehicle classes. These methods are very well publicized.

This paper documents the vehicle response of a tractor-semitrailer following a sudden air loss (Blowout) in a steer axle tire while traveling at highway speeds. The study seeks to compare full-scale test data to predicted response from detailed heavy truck computer vehicle dynamics simulation models. Full-scale testing of a tractor-semitrailer experiencing a sudden failure of a steer axle tire was conducted. Vehicle handling parameters were recorded by on-board computers leading up to and immediately following the sudden air loss. Inertial parameters (roll, yaw, pitch, and accelerations) were measured and recorded for the tractor and semitrailer, along with lateral and longitudinal speeds. Steering wheel angle was also recorded. These data are presented and also compared to the results of computer simulation models. The first simulation model, SImulation MOdel Non-linear (SIMON), is a vehicle dynamic simulation model within the Human Vehicle Environment (HVE) software environment.

This paper is the fourth printed listing of the National Highway Traffic Safety Administration’s (NHTSA) center-of-gravity (CG) location measurements. The previous papers contained data for 1024 vehicles. This paper includes data for 448 additional vehicles tested as part of NHTSA’s New Car Assessment Program (NCAP) for the years 2009 through year 2020. The NCAP involves only the CG location measurement; so the vehicles listed in this paper do not have inertial data.