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<div class="section abstract"><div class="htmlview paragraph">Video recordings of vehicular collisions have become widely available to the accident reconstructionist and can play a vital role in determining the locations and speeds of the subject vehicles involved in a collision. However, due to varying video resolutions, framerate, lens distortion, motion blur, and camera movement, errors in video analysis can occur. To understand the total error inherent to video analysis, this study presents analysis of videos from different video systems, the limitations in the analysis, and a comparison of video analysis speeds to reference datasets. The factors that influenced variance included resolution, lens correction, shutter speed, and framerate. The video systems analyzed included three moving cameras and two stationary units.</div><div class="htmlview paragraph">In the present study, a mock collision scenario in which a target vehicle approached a recording vehicle head-on, was staged to emulate an actual event captured on video. The target vehicle’s speed was analyzed using the captured videos. The video analysis results were then compared to the speeds obtained from the target vehicle’s wheel speed sensors via a VBOX system connected to the vehicle’s Controlled Area Network (CAN), and VBOX GPS position data.</div><div class="htmlview paragraph">The videos were recorded from two locations. Location 1 was at the top corner of a business complex. This location was equipped with two video cameras: a GoPro HERO5 and a Sony α6400 mirrorless camera. Location 2 was within the recording vehicle itself. This location was equipped with three video systems: a 2018 Tesla Model 3 Dashcam video camera system, a generic dashboard video system with a low framerate, and a Blackmagic Design camera. Videos were captured as the recording vehicle moved towards the target vehicle; the camera’s location and angle relative to the target were constantly changing.</div><div class="htmlview paragraph">Errors in the determined speeds were quantified based on comparison of the video analysis speeds to the reference datasets. The errors in speed were determined to be inversely correlated to the video resolution. Additionally, the analysis of the video footage from the stationary source yielded lower error than the analysis of the moving vehicle video for a given resolution and framerate.</div></div>

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<div class="section abstract"><div class="htmlview paragraph">PC-Crash is an accident reconstruction program allowing the user to perform simulations with multibody objects that collide or interact with 3D vehicle mesh models. The multibody systems can be a pedestrian, a motorcycle, or a motorcycle with a rider. The multibody systems are comprised of individual rigid bodies connected by joints. The bodies can be of various size and stiffness along with varying coefficients of friction and restitution. Additionally, the joints can be tailored to define pivot types and range of motion. The current motorcycle models in PC-Crash are generic and do not resemble a sport bike type motorcycle. They are only globally scalable such that you cannot adjust length, width, or height independently. However, the user can adjust each body and/or joint individually as needed.</div><div class="htmlview paragraph">A model was created that resembled a modern sport bike motorcycle. In addition, a multibody rider was mounted on the motorcycle in a typical sport bike riding position. The model was validated using the three instrumented tests that were the subject of previous research (SAE 2020-01-0885). The aforementioned tests involved moving motorcycles striking moving cars. Impact configurations involved the classic T-bone type accident, while others were more complex in nature involving significant longitudinal and lateral impact forces to the motorcycle.</div><div class="htmlview paragraph">The test results were compared to parameters calculated in PC-Crash. Results such as Delta-V, yaw rate and overall post impact trajectories of the motorcycle, rider and movement of the target vehicle were compared to the data from the instrumented test vehicles. In total the multibody rider/motorcycle model was able to simulate the three staged crash tests very well. The overall trajectories of both the rider and motorcycle aligned very well with the overall motion seen in the crash test.</div></div>