The recent events in the Canadian Superbike series at Canadian Tire Motorsport Park and Le Circuit Mont-Tremblant have prompted me to address the safety benefits of using data acquisition, which are often overlooked and deserve more attention. While I did not know Christian Auger or John Ross MacRae, my thoughts and sympathies are with their families and friends.
Just as we can use data to find areas where a rider can save time at the racetrack, the same data provides insight into how close the rider is to not only the traction limits of the machine, tires and track, but also his or her own personal limits.
Much of the useful information comes from GPS data – including lateral and longitudinal acceleration – which is inherent in any GPS-enabled system. The chart attached here shows math channels derived from GPS data from the AiM EVO4 systems used on Jodi Christie’s CSBK Honda CBR1000RR at Le Circuit Mont-Tremblant but we also have the same data available to us through the team’s AiM Solo GPS lap timer.
The building block of looking at GPS data from a safety standpoint is that the maximum lateral and longitudinal acceleration values over the course of a lap should not exceed certain values. In part, those values are set by the motorcycle and how much traction is available from the tires – for example, the typical maximum lateral (cornering) and longitudinal (braking) acceleration numbers I see for a rider on a street bike on street tires are about 1 g on a flat, level turn. That said, of course, less experienced riders will generate less aggressive numbers, while more experienced riders will somehow achieve higher values – never mind the physics.
For riders new to the track, looking at the raw braking and cornering acceleration data will show how close they are to the limit. While this may seem obvious and moot, you would be surprised at how often new riders, with no previous experience to draw from, will right away tread dangerously close to the maximum traction limits in braking or cornering. If the rider brakes at .75 g at every corner on the track but one, and braking at that corner is .95 g, that is cause for a closer investigation.
Once a rider gains experience and becomes familiar with those basic limitations, their techniques turn to combining braking and cornering (trail-braking) or accelerating and cornering (I call this trail-acceleration for lack of a better term). Likewise, the lateral and longitudinal acceleration data can be combined using math channels to show graphically just how much the two are used together. Again, there are physical limits due to the motorcycle and how much traction is available, and there are the rider’s comfort levels. Any numbers out of line – either with what is expected or what is usual for the rider and track – need a closer look.
With even more experienced riders, the raw data and simple math channels don’t show the whole story. The data shown here represents front (blue) and rear (red) weight or load on the bike’s tires – what you would see if you placed a scale underneath. This takes into account the added load from cornering as well as weight transfer from braking and acceleration, and shows a much sharper picture than the acceleration data alone does.
Just as we are interested in maximum values on the acceleration data, here we are concerned with high loads at certain times on the track. Note that maximum load on the front end comes trail-braking uphill into a corner near the end of the lap; the load on the front tire is 400 kg, more than 50 percent more than the combined weight of Jodi and the CBR. Shortly after, the rear tire is under its maximum load of 370 kg, as Jodi accelerates while leaned over through a dip in the track.
These channels represent a first approximation of load. Because GPS data includes altitude and slope, it’s possible to take those changes in elevation into account, modifying the load estimates accordingly. And, if data for the elevation of the inside and outside of the track is available, it’s possible even to include camber into the equations. This gives an accurate indication of how close the rider is to the physical limit at any given point on the racetrack, and is just one way data acquisition can be used for safety purposes rather than performance.