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Most collision reconstructions implicitly assume the same tire/road friction coefficient for all vehicles, despite evidence that friction varies between tires, surfaces, and individual trials. Here we assess the errors introduced by an assumption of a single, universal friction coefficient when reconstructing a collision where vehicles actually had different tire frictions. We used Monte Carlo methods to generate 20,000 synthetic two-vehicle impacts and rest positions using different, randomized friction coefficients for each vehicle and randomized impact speeds. These rest positions were then used to reconstruct both vehicles’ impact speeds assuming a single, common friction coefficient. High and low bounds on the impact speeds were reconstructed using high and low bounds on the common friction. We found that more than 97% of the true impact speeds were in the ranges reconstructed using upper and lower friction bounds.

Apple introduced a lidar-based depth sensor and enhanced Augmented Reality (AR) application programming interface (API) to the 2020 iPad Pro and iPhone 12 Pro, making widespread use of 3D scanning possible. Here we quantified the bias and repeatability of the 3D scans made using this system. The exterior and interior of a single vehicle and the exterior of a filing cabinet were scanned four times by eight different operators. Each operator then extracted four measurements from each of the exterior scans, five measurements from each of the interior scans, and three measurements from each of the filing cabinet scans. Hand measurements using a string and tape measure were used as reference values to estimate the bias. The values extracted from the 3D scans were biased between 0.9 and 9.3 cm below the actual exterior measurements and between 2.2 cm below and 0.3 cm above the actual interior measurements.

The critical curve speed formula used for estimating vehicle speed from yaw marks depends on the tire-to-road friction and the mark’s radius of curvature. This paper quantifies uncertainty in the radius when it is determined by fitting a circular arc to three or more points. A Monte Carlo analysis was used to generate points on a circular arc given three parameters: number of points n, arc angle θ, and point measurement error σ. For each iteration, circular fits were performed using three techniques. The results show that uncertainty in radius is reduced for increasing arc length, decreasing point measurement error, and increasing number of points used in the curve fit. Radius uncertainty is linear if the ratio of the standard deviation in point measurement error (σ) to the curve’s middle ordinate (m) is less than 0.1. The ratio σ/m should be less than 0.018 for a radius found using a 3-point circular fit to be within 5% of the actual value 95% of the time.

Accident reconstructionists rarely have complete data with which to determine vehicle speed, and so the true value must be bracketed within a range. Previous work has shown the effect of friction uncertainty in determining speed from tire marks left by a vehicle in yaw. The goal of the current study was to assess improvements in the accuracy of vehicle speed estimated from yaw marks using progressively more scene and vehicle information. Data for this analysis came from staged S-turn maneuvers that in some cases led to rollover of sport utility vehicles. Initial speeds were first calculated using the critical curve speed (CCS) formula on the yaw marks from the first portion of the S-maneuver. Then computer simulations were performed with progressively more input data: i) the complete tire marks from the whole S-maneuver, ii) measured vehicle mass, iii) measured suspension stiffness and damping, and iv) measured steering history.

Modern vehicles record dynamic data from a number of on-board sensors for events that could precede a crash. These data can be used to reconstruct the behavior of a vehicle, although the accuracy of these reconstructions has not yet been quantified. Here, we evaluated various methods of reconstructing the vehicle kinematics of a 2017 and a 2018 Toyota Corolla based on Vehicle Control History (VCH) data from overlapping events generated by the pre-collision system (PCS), sudden braking (SB) and anti-lock brake (ABS) activation. The vehicles were driven towards a stationary target at 32-64 km/h (20-40 mph) and then after the pre-collision alarm sounded the vehicle was steered sharply right or left and braked rapidly to rest. VCH data for PCS event were recorded at 2 Hz and for the sudden braking and ABS activation events at 6.7 Hz.

Numerical parameters describing suspension stiffness and damping are required for 3D simulation of vehicle trajectories, but may not be available. This paper outlines a simple, portable method of measuring these properties with a coefficient of variation of 5% on stiffness. 24 of 26 vehicles tested were significantly stiffer in roll than pitch, complicating analyses with models that don't include anti-roll. Suspension parameters did not correlate with static wheel load distribution, and damping coefficient did not correlate with natural frequency. Computer simulations of the speed required to initiate rollover in an S-curve were highly sensitive to the suspension parameters used. When pre-impact tire marks and rollover distance were considered, the simulations became almost insensitive to suspension parameters.

PC-Crash simulations of staged collisions require dozens of parameters describing vehicle and impact parameters. The Collision Optimizer will vary initial speeds and impact parameters to obtain a best fit to a desired end state, but vehicle parameters are left unchanged. The present paper allows these other parameters to vary in thousands of combinations, re-optimizing the solution in each to find the relationships between the previously fixed parameters and the resulting impact speeds. The results show that tire friction and vehicle inertial properties have the most influence on impact speeds. Other parameters have little influence on the results.

In low-speed collisions, motor vehicles can lose a significant fraction of their initial kinetic energy without plastic deformation or damping elements in their bumper assemblies. Five vehicles were subjected to multiple, non-damaging barrier and vehicle-to-vehicle impacts. Position, velocity, acceleration and force data were recorded for all collisions. Modeling vehicles as non-rigid two degree of freedom systems accurately predicted velocity and restitution responses for five vehicles in barrier and vehicle-to-vehicle impacts.

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.

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.

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.

Accurate values of tire-roadway friction are an essential requirement for an accurate collision reconstruction. This paper presents updated tire friction data for three grades (economy, touring, and performance) of commercially-available tires under both wet and dry road conditions. Differences between tires and road conditions were tested using 540 locked wheel braking tests with a single passenger vehicle on a single road surface over six consecutive days. The vehicle was braked from about 60 km/h to a stop using a mechanical brake actuator to minimize variations in brake pedal application. These results showed differences between the friction measured with economy, touring and performance tires under wet and dry road conditions. Dry road friction values were higher than those reported previously in the literature using older model tires and these dry road friction values were normally distributed.