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Bicycle computers record and store global position data that can be useful for forensic investigations. The goal of this study was to estimate the absolute error of the latitude and longitude positions recorded by a common bicycle computer over a wide range of riding conditions. We installed three Garmin Edge 530 computers on the handlebars of a bicycle and acquired 9 hours of static data and 96 hours (2214 km) of dynamic data using three different navigation modes (GPS, GPS+GLONASS, and GPS+Galileo satellite systems) and two geographic locations (Vancouver, BC, Canada and Orange County, CA, USA). We used the principle of error propagation to calculate the absolute error of this device from the relative errors between the three pairs of computers. During the static tests, we found 16 m to 108 m of drift during the first 4 min and 1.4 m to 5.0 m of drift during a subsequent 8 min period. During the dynamic tests, we found a 95th percentile absolute error for this device of ±8.04 m.

The sampling rate and synchronization of the pre-impact data stored by General Motors (GM) sensing and diagnostic modules (SDMs) have not been experimentally determined. The goals of this study were to measure the time shift between the SDM-reported data times and algorithm enable, sampling rate variation and the synchronization of the sensor data. In this study, two experiments were performed. First, the SDM of a 2002 Pontiac Sunfire was artificially triggered while the throttle position, engine speed, vehicle speed and brake signals were also being monitored at their source sensors. Second, the throttle and vehicle speed sensors were replaced with artificially generated inputs so the timing of the SDM recorded values could be compared to that of the known inputs. Sampling rate and data synchronization were determined by fitting the SDM recorded values to the measured sensor outputs.

The Vetronix Crash Data Retrieval (CDR) system can download information recorded by the restraint control module (RCM) of newer Ford vehicles. In an earlier study, a 2003 Ford Crown Victoria and a 2003 Ford Windstar were exposed to 84 staged collisions with speed changes up to 13.5 km/h. After each test, crash data was downloaded from the RCM using version 2.1 of the Vetronix CDR software. In this study, the crash data was re-analyzed using the current version 2.4 of the software. Unlike version 2.1, version 2.4 did not report duplicate data points. Version 2.4 reported more accurate speed changes for the Windstar (average underestimate of 0.23 km/h) RCM but less accurate speed changes for the Crown Victoria (average underestimate of 0.73 km/h).

Crash data stored in the airbag sensing and diagnostic modules (SDMs) of General Motors vehicles can provide useful information for accident investigators. To quantify the accuracy and sensitivity of select 2003 to 2004 SDMs, two types of tests were performed. First, three 2004 vehicles underwent 136 vehicle-to-barrier and vehicle-to-vehicle collisions with speed changes up to 8 km/h. Second, 2003 and 2004 model year SDMs underwent a range of crash pulses using a linear sled. In all of the tests the speed change reported by the SDM underestimated the actual speed change. The speed change underestimates ranged from 0.2 to 2.9 km/h except for several anomalous tests in which the underestimate was as high as 12.3 km/h. The magnitude of this error varied with crash pulse shape. Increasing crash pulse duration and decreasing peak acceleration increased the difference between the actual and SDM reported speed change. The threshold accelerations for the SDMs tested ranged from 1.1 to 2.7g.

Two methods for calculating speed from curved tire marks were investigated. The commonly used critical speed formula and a computer simulation program were evaluated based on their ability to reproduce the results of full-scale yaw tests. The effects of vehicle braking and friction coefficient were studied. Twenty-two yaw tests were conducted at speeds between 70 and 120 km/h. For half of the tests, about 30% braking was applied. Using the measured sliding coefficient of friction, both the critical speed formula and the computer simulations under-predicted the actual speed of the vehicle. Using the measured peak coefficient of friction, both methods over-estimated the actual speed. There was less variance in the computer simulation results. Braking tended to increase the speeds calculated by the critical speed formula.

Crash data recorded by the restraint control module (RCM) installed in newer Ford passenger vehicles have recently become available to investigators. To quantify the accuracy of the crash data in low-speed collisions, two RCM-equipped vehicles were exposed to 84 aligned frontal barrier collisions with speed changes up to 13.5 km/h. The accuracy of the speed change reported by the RCM ranged from an underestimate of 1.8 km/h to an overestimate of 0.3 km/h. The error varied with speed change. The RCMs were mounted on a linear sled to investigate their sensitivity to specific collision pulse parameters. For both RCMs, the first eight acceleration data points were duplicated at the end of the data and the record of the crash pulse was often incomplete. Based on the results of this study, crash investigators need to carefully interpret the RCM-reported acceleration and speed change data before using it to reconstruct low-speed collisions involving Ford vehicles.

Most 1999 and newer General Motors (GM) vehicles have an event data recorder (EDR) that can record pre-crash speed incorporated into the airbag sensing and diagnostic module (SDM). The accuracy of the SDM-reported pre-crash speed over a wide range of speeds has not been previously tested and reported. In this study, the SDMs of three late-model GM passenger cars were artificially triggered while driving at a constant speed between 1 and 150 km/h. The SDM-reported pre-crash speeds were compared to speeds measured by a calibrated 5th-wheel of known accuracy. The results showed that the accuracy of the SDM-reported pre-crash speed varied with both speed and vehicle. The overall uncertainty associated with all three SDMs tested varied from a 1.5 km/h overestimation of vehicle speed at low speeds to a 3.7 km/h underestimation of vehicle speed at high speeds.

Adrian's visibility model is a useful tool for assessing the visibility of an object at night. However, it was developed under laboratory conditions. Thus, it is necessary to determine the visibility levels which are required for detection under nighttime driving conditions. Experimental data from Olson et al were applied to the Adrian visibility model to determine visibility levels at target detection for alerted drivers. The data has been modified to account for experimental delay in the recorded detection points and a correction has been applied to assess driver expectation. Driver age, headlight beam pattern, and target reflectivity were all found to have a significant effect on visibility level at target detection. For alerted drivers, 50th-percentile threshold visibility levels between 1 and 23 were calculated. For unalerted drivers, 50th-percentile threshold visibility levels between 13 and 210 were calculated.

Collision data stored in the airbag sensing and diagnostic module (SDM) of 1996 and newer GM vehicles have become available to accident investigators through the Vetronix Crash Data Retrieval system. In this study, two experiments were performed to investigate the accuracy and sensitivity of the speed change reported by the SDM in low-speed crashes. First, two SDM-equipped vehicles were subjected to 260 staged frontal collisions with speed changes below 11 km/h. Second, the SDMs were removed from the vehicles and exposed to a wide variety of collision pulses on a linear motion sled. In all of the vehicle tests, the speed change reported by the SDM underestimated the actual speed change of the vehicle. Sled testing revealed that the shape, duration and peak acceleration of the collision pulse affected the accuracy of the SDM-reported speed change. Data from the sled tests were then used to evaluate how the SDM-reported speed change was calculated.

An anthropomorphic test device (ATD) which accurately models the kinematic and kinetic responses of human subjects during head restraint contact in low-speed rear-end collisions is needed to evaluate present and future seat and vehicle designs. The primary goal of this study was to quantify the biofidelity of a new rear-impact ATD, the RID2a, by comparing its dynamic response to those of human subjects under identical test conditions. For this study, a RID2a and a Hybrid III ATD were each exposed to 10 low-speed rear-end collisions: five at a speed change of 4 km/h and five at a speed change of 8 km/h. Sagittal plane kinematics of the head and upper torso, head restraint contact forces, and the reaction loads and moment at the atlanto-occipital joint were determined and compared to the response of eleven male human subjects. Both ATDs produced repeatable response corridors. As observed by others, the Hybrid III did not replicate many features of the human response.

The purpose of this paper was to compare the damage to pickup truck bumpers produced by vehicle-to-barrier and vehicle-to-vehicle collisions of a similar severity, in order to determine whether vehicle-to-barrier tests can serve as surrogates for vehicle-to-vehicle tests in accident reconstruction. Impact tests were conducted on the front and rear bumpers of five pickup trucks. Each truck was subjected to an impact with a fixed barrier and with a passenger vehicle. All impacts resulted in pickup truck speed changes of about 8 km/h. Damage produced in the barrier and vehicle-to-vehicle collisions was similar if both collisions resulted in bumper mount damage on the pickup truck. If there was no bumper mount damage, then the bumper beam deformation depended on the shape of the impactor.

Five collisions were staged in order to evaluate PC-Crash, a simulation program used for investigating motor vehicle collisions. Both vehicles were moving in all of the staged collisions at 1:1 or 2:1 speed ratios. Pre-impact speeds ranged from 19 to 56 km/h. Two separate methods were used to test the validity of the simulation program. Firstly, collision parameters were calculated from measured data, and used as input to the PC-Crash collision model. Secondly, the post-impact vehicle paths and rest positions were used to determine the pre-impact speeds. There was agreement between measured and simulated collision dynamics. Using the PC-Crash "Optimizer" to reconstruct the five collisions, the error in calculated pre-impact speeds of the ten vehicles ranged from-3.3 to +4.1 km/h. Vehicle speeds were determined based on post-impact rotation and paths, without detailed information on the braking from each wheel or the actual collision coefficient of restitution.

Limited data exist which quantify the kinematic response of the human head and cervical spine in low-speed rear-end automobile collisions. The objectives of this study were to quantify human head/neck kinematics and how they vary with vehicle speed change and gender during low-speed rear-end collisions. Forty-two human subjects (21 male, 21 female) were exposed to two rear-end vehicle-to-vehicle impacts (speed changes of 4 kmlh and 8 km/h). Accelerations and displacements of the head and torso were measured using 6 degree-of-freedom accelerometry and sagittal high speed video respectively. Velocity was calculated by integrating the accelerometer data. Kinematic data of the head and C7-T1 joint axis in the global reference frame, and head kinematic data relative to the C7-T1 joint axis are presented. A statistical comparison between peak amplitude and time-to-peak amplitude for thirty-one common peaks in the kinematic response was performed.

In order to assess the head and neck kinetics of human subjects exposed to low-speed rear-end impacts, a method for measuring the magnitude and line of action of the force between the head and the head restraint was required. In addition to being accurate and repeatable, the design was required to maintain original seat back and head restraint geometry, mass, stiffness, and height adjustment. This paper presents a design using strain gauges applied to the head restraint tubes, upper seat back, and custom replacements for brackets attaching the head restraint to the seat back. The background theory and free-body analysis, the analog math circuitry, and a dynamic calibration procedure are presented. Overall force magnitude and line-of-action errors are quantified, and a sample output from a human subject undergoing a rear-end collision with a speed change of 8 km/h is presented.

Front, rear, lateral and side-swipe collisions were staged to correlate passenger vehicle damage to motion. Data from the staged collisions are used to develop severity-prediction methods for the four collision types. Human volunteers were present in many of the vehicles tested. Their responses, and the responses of human volunteers to staged impacts in other studies, are discussed in terms of impact severity. For front and rear impacts, data are presented that correlate the post-impact condition of bumper systems to impact severity. These data build on data previously presented1,2,3. A method for computing velocity change (ΔV) for vehicle to vehicle collisions from vehicle to barrier data is presented. Data from staged low-speed lateral collisions correlate target and bullet vehicle damage to linear and angular velocity change (ΔV, Δω), impact location, pavement friction and collision force.