While the AiM gave me a much richer stream of data, I still used the Waylens for video. This resulted in a somewhat elaborate, offline batch-process to dump the data and render/export the captured video in a useful way. Once that pipeline was finished I could put both into TrackAttack to do analysis, which worked reasonably.
The only problem was that it was days after I was done with an event before I got around to getting all this done. At that point, the value of the data and video diminished somewhat because I could only act on it weeks or even months later at my next track-day. So over the winter I’ve been thinking about how to close that loop so that I can get more value out data-logging and analysis.
Amidst all of this, Garmin releases the Catalyst, to great accolades. Several track-day buddies I know have already switched to these and are planning on putting their gently-used AiM units on Craigslist or Facebook. I have to admit, this unit looks pretty cool and it definitely had me thinking about doing the same thing.
However, two things held me back from making the switch. The first was an inclination to keep what I had due to sunk-costs. I had already bought the AiM unit and paid for the work to get it installed in the CAN bus, which provides a wealth of data. In total, this wasn’t cheap and the thought of undoing it all and ponying up another grand for a new unit wasn’t very appetizing. A living example of the sunk-costs fallacy? Perhaps.
But the other thing was that, as cool as the Catalyst is, it appears to be somewhat of an opaque walled-garden (I know, a hilarious accusation from a longtime Apple user). As of the time of this writing, there isn’t a way to dump any logging data out of it for analysis in third party tools like TrackAttack. Similarly, the video isn’t easily accessible, especially with data overlays. Now, this is not to say that such features aren’t in the future, but the lack of them now was enough to keep me wary.
While the magical coaching feature of the Catalyst looks awfully compelling, there is a (perhaps stubborn) part of me that wants to learn how to do the data analysis myself. I have no doubt that it will be a slower and steeper climb, but I have faith that it will be more rewarding in the end.
Anyway, back to my immediate conundrum—what to do? Thus far I had avoided using any of AiM’s software as it was very…Windows-y. I’ll admit it—I found the presentation and UX to be pretty off-putting. But I decided to give it another go and learn how it actually works, without passing judgment on it. It didn’t take long before I learned that the software is quite powerful, but there are actually a tremendous amount of useful resources on the web (particularly James Colburn’s YouTube training videos).
It was at this point I could see how I might put-together a workflow for both quick post-session and deeper at-home analysis and decided to double-down on the ecosystem. The first thing I was going to need was some device to pull data off of the AiM system in the paddock after a session. I was pretty sure AiM’s Race Studio wasn’t do anything too magical or CPU-intensive so I decided on finding the most compact tablet/laptop I could.
As a longtime UNIX and Mac guy, the Windows world is still pretty foreign to me. But last spring I bought a gaming PC (mostly for sim-racing purposes) and started becoming a little more fluent with the ecosystem. I was pleasantly surprised at how nice Window 10 is to use, and I was considering purchasing a device that had never before been on my radar—a Microsoft Surface Go 2.
I purchased a unit and after using for just a few days, I have to admit that I am impressed. For a small, compact Windows machine it ticks all the boxes for what I want to do. Sure, it’s not without its compromises, but thus far it’s looking like the perfect solution to in-paddock lap analysis.
My next trick is to see if I can dive further into the AiM ecosystem and get a SmartyCam working with this. Stay tuned…