Tag Archives: Collision Reconstruction

May is Motorcycle Safety Month

Robert T. Lynch, P.E., Principal Collision Reconstruction Engineer

As the warmer weather approaches, more and more motorcycles are hitting the streets. Coming out of the winter months, where you would rarely see a motorcycle in operation in the northern half of the country, automobile operators must retrain their brains to specifically “Watch for Motorcycles.” In particular, drivers should take an extra moment to scan the oncoming lane for motorcycles prior to executing a left turn. Intersections introduce the greatest potential for vehicular conflict, and not surprisingly, account for the overwhelming majority of motorcycle (and automobile) collisions.

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“Speed (and more) From Video”

James R. Schmidt, Jr., BSME, Sr. Collision Reconstruction Engineer

Surveillance cameras are becoming increasingly more prevalent in today’s world, and videos from these surveillance cameras are oftentimes invaluable to an accident reconstruction analysis.

In this example, the movement of a municipal trash truck is captured by a residential video doorbell. This is just an example, as the trash truck was simply making its routine weekly pickup and was not involved in a collision. We sharpen our skills by evaluating such videos for practice.

Surveillance Trash Truck

As you’ve likely seen in our other examples, we can evaluate the speed of a vehicle from video, even if the video shows only a fraction of a second of the vehicle’s movement. In this example, we’ve evaluated average speed of the trash truck as it moves from one stop to the next. Inherently included in this evaluation is the distance the truck traveled over this same time period, being that speed is calculated from distance divided by time. Truck acceleration from a stop and deceleration back to a stop could also be calculated, if necessary. You can see the acceleration in the increasing speed at the beginning of the plot, then the speed tops out at about 8 mph, after which the speed decreases, which is the deceleration heading toward the next stop.

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Does Wearing Yellow Glasses Improve Nighttime Visibility?

Yellow Sunglasses

Robert T. Lynch, P.E., Principal Collision Reconstruction Engineer

Despite claims that yellow glasses improve night vision, a recent study by Harvard researchers indicates that subjects wearing yellow glasses at night responded a fraction of a second slower than those not wearing the glasses.

Yellow glasses filter out blue light, which tends to get scattered more than red light due to its shorter wavelength. This scattering of blue light is said to contribute to headlight glare; so, the theory behind yellow glasses is that by filtering out the blue light, the glare is reduced, and night driving is improved. However, the white light emitted from vehicle headlights is made up of all the wavelengths of the visible spectrum, and by filtering out blue light, the total amount of light reaching the eye is reduced.

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Automatic Emergency Braking Doesn’t Always Prevent Pedestrian Collisions

Breaking Technology

Robert T. Lynch, PE, Principal Collision Reconstruction Engineer

Automatic Emergency Braking (AEB) is generally designed to automatically apply the brakes when a rear-end vehicle collision is imminent. This technology has been shown to mitigate rear-end impacts; however, this technology is not always capable of detecting pedestrians crossing in front of a vehicle.

AAA has conducted testing of vehicles equipped with AEB and found that in 60% of the tests, the vehicle failed to stop, from an initial speed of 20 miles per hour, before striking the pedestrian dummy. The testing was performed during daylight hours with adult pedestrian dummies. The tested vehicle performed worse at higher speeds, under dark conditions, and with child dummies.

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Speed from Video – Captain Video’s Specialty

James R. Schmidt, Jr., BSME, Sr. Collision Reconstruction Engineer ::::

I’m a collision reconstruction engineer with over 23 years of experience in the field. I’m affectionately known in the office as Captain Video, given my love for the evaluation of vehicle speed and crash-related parameters from surveillance videos.

Basic evaluation from stationary camera:

A 2019 Toyota Sienna minivan passes in front of a stopped dash cam. Speed from video is evaluated therefrom. Speed is distance over time. The easiest way to perform the evaluation is to look at the timeframe required to travel the vehicle’s wheelbase (i.e. the distance from the front wheel or axle to the rear wheel or axle). So, in this example, the minivan travels its 119 inch wheelbase in 7 frames of a 30 frame-per-second video. Distance is 119 inches, or 9.92 feet. Time is 7 frames divided by 30 frames per second, or 0.233 seconds. Calculating speed … 9.92 feet divided by 0.233 seconds is 42.5 feet per second, or ~29 mph. FYI, this was a 35mph speed limit roadway.

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Was it Road Rage? Can an Engineering Analysis Answer That Question?


Justin P. Schorr, Ph.D, Principal Collision Reconstruction / Transportation Engineer ::::

A collision occurred when a school bus moved from the right lane of a limited access highway onto the rightside shoulder and contacted a disabled vehicle. It was dark and the disabled vehicle on the shoulder was not illuminated. The operator of the school bus testified that a tow truck located in the lane to his left executed a lane change, forcing him off the roadway and onto the shoulder into the disabled vehicle. The operator of the tow truck testified that the disabled vehicle was his intended “pickup,” but as he went to move from the center lane to the right lane to access the disabled vehicle, the school bus was trying to squeeze by him by passing him on the right, resulting in the collision.

Two forms of event data were available for analysis – video from the tow truck and engine control module data from both the tow truck and the bus. This data allowed for an accurate plotting of the speed of both vehicles prior to and at the time of the incident. Since the vehicles occupied the same place at the same time during the collision (i.e. the tow truck was touching the bus), the event data could be correlated such that their relative positions leading up to the collision could be plotted to scale. The video data also included a rearview camera making it so the position of the bus in the right lane as it approached the tow truck (which was initially in the center lane) could be seen. This data confirmed the independent correlation and plotting of the speed data from each vehicle.

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