Formula 1 (F1) often calls itself "The Pinnacle of Motorsport". Hundreds of millions of Dollars are poured into Formula 1 World Championship teams every year – each – not in total. With billions of Dollars spent and even more earnt every year (TV licenses, prize money, sponsorship deals, track tickets, merchandise, marketing of car manufacturer brands, etc.) it is no surprise that F1 teams shy no effort and cost to improve lap times by mere 1/100s or even 1/1000s of seconds.
Everything, absolutely everything, is scrutinised and optimised to levels other sports don't typically see.
This is high tech in it's most extreme sporting guise. Space age technology cannot be adopted fast enough and innovation is at it's best.
“Humans are the weak point in a system of efficiency and precision.”
With all this focus on detail, efficiency and performance there are some very obvious weak points ... humans.
This season is shaping up to be one of the best in recent history with the two leading teams, Ferrari (my personal favourite) and Mercedes, fighting for top honours early on.
The first few races of 2017 highlighted the key F1 strategy areas where humans can fail, or at least perform to lesser standards than maybe an AI or humans supported by artificial intelligence would.
Drivers make subjective observations about tyre-wear, teams respond with strategies and strategy adjustments, mechanics perform sub 2-second pit-stops with tyre changes, crashes and safety-car phases throw in additional uncertainties (even the type of safety car phase, virtual or real, make a difference) and variables the human mind simply cannot compute fast enough to deal with optimally all the time.
F1 teams have an incredible amount of very precise track data and they know at any time precisely where their cars are (GPS) and what state they are in (telemetry sensors).
The strategy teams at the race track and at the team's HQ are constantly trying to predict the next best optimal move to improve their drivers' positions.
A lot of computation power, bandwidth and no doubt very sophisticated software is involved, all geared towards forecasting/predicting the final outcome of the race.
Despite all of this attention to detail and high-tech paired with experience and skill ... they get it "wrong" quite often.
I wonder how, and at times can't stop to think what it would be like if they applied artificial intelligence and machine learning in real time with "deadly" accuracy.
Of course the current top teams are doing a lot with sophisticated/advanced AI already, or are they? Surely they must be, but it is as many other things in this sport top secret.
It is in the nature of the sport that team members switch teams fairly regularly so I can't imagine if a team was gaining significant benefits from A.I. it would stay a secret for too long.
This is right now though an early adopter's game ... who comes first to make it work will make untold millions more than they already do ... maybe a chance for one of the smaller teams who cannot spend such extreme manufacturing budgets to catch up by dominating the strategy game?
What would some of the first things to focus on be? The "low hanging fruit", if you will.
Pit-Stop Timing
First of all teams could use AI, more specifically machine learning/deep learning to get better predictions of when best to stop the car to change tyres. These better predictions could happen from very early on in a race and become more and more refined with increasing confidence as the laps count down. Such a model could take a lot more parameters into account than current systems and human strategists already do, but it would have to be possible to introduce human judgement for on-the-fly adjustments of the model - like for example when the radio communications of another team indicate a varied situation from the nominal model, e.g. complaints about vibrations due to a flat spot.
To be clear, what I am suggesting here would require truly incredible amounts of race simulation data with far higher level of information detail and density – and of course far higher real-time compute capacity (which is readily available via the cloud).
Tyre Choice
With the choice of when to best box the car (yep, they call it "box" ... has German origins) the decision what tyres to put on has to be made. Strategies and best tyre choice might very quickly vary from moment to moment, especially when the bulk of other cars are stopping on the same or similar laps, but also as another of many examples, if the weathers is changeable. Weather is an especially interesting aspect here, as AI could be used in many more interesting ways to take local short and medium term weather data into account.
Predicting the weather (and temperatures of air and track) is too much, you might say. If F1 teams could guarantee themselves a 1s per lap advantage by having the tyre choice right I can easily see them spending vast amounts of money to do that. There is of course, as with everything in Formula One, a level at which returns are diminishing, no matter what you spend ... so, that has to be kept in mind.
Team Orders
Often enough we see much faster drivers being stuck behind their team mate. They may be faster due to lower tyre-wear, technical issues of the driver ahead or a host of other factors. Often the teams decide to issue team orders, sometimes they don't, but in all cases it is still up to the driver in front to let their colleague through in a reasonable timeframe. Drivers being racers and wanting to be die-hard competitive winners don't always obey these orders because they, well, just don't want to.
Often that harms the teams in terms of constructor's points scored and usually both team members are worse off. There is a strong human/psychology factor here. Nobody wants to be second, in anything, ever, full stop. And nobody wants to be told by (humans) the team that they are not the favourite child of the moment.
A few years ago team orders were still banned, but teams would use them anyway ... and people get upset.
If the drivers however knew that an artificial intelligence, rather than human decision making (which can always be challenged on the basis of past bad calls) was at the root of this "recommendation" they might be more willing to comply, especially if they trust the system to benefit them in future in the same unbiased way.
What is needed to make this shift in F1 strategy happen?
- Data
- More Data
- Even More Data
- Machine Learning Models
- Huge Investment in Compute Power
