To determine if a driver could have avoided striking a pedestrian, the pre-collision speeds of both the vehicle and the pedestrian will usually be needed. Avoidance analysis will begin by determining the relative positioning of the vehicle and the pedestrian in the moments leading up to the collision. Then, the reconstructionist can determine when in moments leading up to the collision, the approaching driver would have been able to detect and perceive the pedestrian as a hazard and how much time was available for avoidance.

In some ways, motorcycle crashes are like any other crash, and attorneys litigating car crashes will already have a sense for where to focus their energy when they encounter a motorcycle crash. There are some unique elements of motorcycle crashes though, that attorneys will benefit from keeping in mind. This post covers some of those unique items.

Empirical throw distance equations have been developed for forward projection trajectories and wrap trajectories, but not for roof vaults, fender vaults, pedestrian carries, or sideswipes. In part, the lack of empirical equations for these trajectory types is due to a lack of staged collisions of those type on which to base an equation; but, more significantly, these trajectory types are likely to produce a wide range of throw distances for any particular impact speed.

Often, questions arise on our cases related to the sight distance a driver had available in the moments leading up to a collision. When these question arise, it may not be sufficient to inspect the site because changes to vegetation could have occurred between the date of the crash and the date of our inspection. In this case study, we give an example of how we can reconstruct the shape and location of vegetation based on the on-scene police photographs and measure the available sight lines.

In this case study, we present an example of incorporating publicly available, exemplar data from Event Data Recorders (EDRs) into an accident reconstruction. In current accident reconstruction practice, it is commonplace to have objective, measured data from at least one of the involved vehicles. This data, which is recorded by on-board vehicle systems, is often obtained with the Crash Data Retrieval (CDR) system. Oftentimes, the correct interpretation of the data from the vehicle is not apparent on the surface and someone needs to analyze its meaning within the context of the crash.

Nathan Rose, Jacob Palmer, and Michael Erickson of Explico reconstructed a nighttime, left turn across path motorcycle crash involving a Chevrolet Malibu and a Suzuki GSX-R750 motorcycle. The Chevrolet turned left across the path of the Suzuki. The left-turning driver stated that she did not see the approaching motorcycle prior to initiating her turn. The officers located a GoPro camera at the scene, which had initially been attached to the motorcyclist’s helmet.

The need to maintain stability of the motorcycle limits the upper end of the decelerations that motorcyclists can be expected to achieve. In relationship to the stability of the vehicle, bicyclists can come up against even more restrictive limits on their braking capabilities since the higher center of gravity of the bicycle-rider combination (over and above that of the typical motorcycle-rider combination) makes bicycles more prone to pitch-over falls.

This case involved the driver of a Volkswagen sedan attempting a left turn across the path of an approaching motorcyclist. A collision occurred between the passenger side of the Volkswagen and the motorcycle. Conditions at the time were dry, clear, and daylight. The speed limit for the motorcyclist was 30 mph. To conduct our analysis, Explico reviewed police photographs and bodycam footage to identify physical evidence from the crash. Physical evidence included debris and fluid from the collision, the rest positions of the Volkswagen and the motorcycle, damage to the vehicles, and a scrape on the roadway surface.

Suppose that there is physical evidence that the front wheel and tire of the motorcycle was initially pushed directly rearward into the frame, engine, or radiator of the motorcycle. Well, in a case like this, we could quantify the rearward movement of the front wheel and tire (and the resulting wheelbase reduction) needed to bring the front wheel or tire into contact with these components. Applying this measurement within the wheelbase reduction equations would be likely to underestimate the motorcycle speed (or the closing speed of the collision, if the struck car had velocity towards the motorcycle).

Recently, PC-Crash simulation software [1] incorporated a single-track vehicle driver model for simulating motorcycle motion. In the past, a two-wheeled vehicle in PC-Crash would simply fall over - it had no stability. With the single-track vehicle driver model, the model will generate the necessary steering inputs and lean for the motorcycle to follow a curved path while remaining upright and stable.

In a prior post, I examined how certain motorcycle characteristics affect the deceleration when the motorcycle is sliding on the road: fairings, crash bars, frame sliders, etc. That post did not address the influence of a wet road. In this post, I offer a technical note to document studies that have shown that a wet roadway reduces a sliding motorcycle’s deceleration.

In addition to a longitudinal pavement edge, a motorcyclist could lose control and capsize due to interaction with the far face or edge of a pothole. The loss of control mechanism in this scenario could be a significant front wheel diversion or significant slowing of the front wheel that causes pitching. To cause either, the contact or collision between the tire and the far edge of the pothole would have to be of sufficient severity to cause such a diversion or slowing.

One example of a roadway feature that could contribute to a fall by a motorcyclist is a longitudinal pavement edge. A longitudinal edge could be encountered by a motorcyclist as one side of a lengthy pothole or area of road deterioration. Another example occurs on a roadway that is being repaved, particularly when one lane has already been repaved and the adjacent lane has not. In such instances, the repaved lane may sit higher than the adjacent lane. A motorcyclist that maneuvers from the lower lane to the higher lane could encounter a longitudinal pavement edge of sufficient height to contribute to a loss of control and a capsize.

Robins [1] observed that “in spite of the many converging cues available to humans to judge the separation distances and speeds of other objects, we perform poorly at such tasks.” Similarly, Olson [2] observed that “witnesses’ judgements of distance in the usual measuring units are likely to be unreliable.” Despite the limitations of humans to judge distance, witnesses and drivers involved in car crashes are often asked to use the unit of a car length to estimate how far they were from another vehicle at some point prior to a collision.

We can make the example of the prior post more nuanced by incorporating the influence of eccentricity [1] and time-to-collision (TTC) [2] into our evaluation of the motorcyclist’s PRT. This will lead us to a different PRT for the motorcyclist in the left lane versus the motorcyclist in the right lane. For a motorcyclist that needs to respond to the left-turning vehicle, the eccentricity for the motorcyclist in the left lane will be in the range of 3-6 degrees. For the motorcyclist approaching in the right lane, the eccentricity will be in the range of 5-9 degrees. For a motorcyclist in the left lane who needs to respond with braking, the TTC when the left turn begins would be in the range of 1.5 to 3 seconds. For motorcyclists in the right lane who need to respond, the TTC would likely be in the 2.5 to 4 second range.

Many motorcycle crashes occur when a driver attempts to cross the path of an approaching motorcyclist at an intersection. Common patterns emerge in these crashes. This post begins exploring these collisions and builds a framework for understanding their common elements. When it comes to assessing causation in motorcycle crashes at intersections, the details matter, and the issues raised in this post will need to be considered on a case-by-case basis.

Crash reconstructionists are frequently asked to determine how a crash could have been avoided. In conducting this analysis, there will be instances in which an assumption will be made about the level of deceleration a motorcyclist should have been able to achieve. This post explores the decelerations achievable by motorcyclists.

In July of 2017, I published a blog post summarizing literature and data related to typical decelerations of motorcycles that capsize and slide to rest. This post expands on that prior post, specifically extending the discussion of how certain motorcycle characteristics affect the deceleration: fairings, crash bars, frame sliders, and the like. As always, I welcome your comments, feedback, and questions.

High-speed, single vehicle rollovers can often be analyzed in three phases: the loss-of-control phase, the trip phase, and the roll phase. Analogously, a single-vehicle motorcycle crash can often be analyzed in three phases: the loss-of-control phase, the capsizing phase, and the sliding and tumbling phase.

An animation can provide tremendous value to a jury by making an expert’s opinions more understandable. In this article, I will give you 7 principles for producing credible and admissible animations for use in depositions, mediations, settlement conferences, and trials. These are the same principles that I and others in my firm have used to develop numerous animations that have been admitted in trials across the United States.