Actually, in some aspects, we're already there! :)
Computers can run a car at the limits of its traction circle much better than humans can, even with nothing more than an accelerometer. I've done some hacking around this, and it turns out that you can go about it fairly naïvely, code-wise, and do way better than even good amateur racers. Unfortunately, I don't have access to any top F1 drivers to datalog them side by side with my software, but maybe someday.
In terms of the quickest path around a given course, computers are _way_ better than humans at that. There's the official "racing line", which is a generally accepted fastest line, but as you can create more acceleration, the line actually flattens out towards the shortest route through a given section. Take a look at something like Spec Miata vs an AWD time attack car for a great demonstration of this.
Humans still have the edge on understanding & interpreting the environment. There's a lot of work being done in this area, though - I suspect we'll get some fairly robust stuff that's available to us on the open market in a few years.
Then there's the fatigue factor.
Heck, if nothing else, having to cart around 150-180lbs of meat with fairly tall packaging and some weird requirements about having to see and what kind of forces you can exert on it is a bit of a drag to making the quickest possible wheeled vehicle.
Sorry for the wall of text - this is something I've been hacking around a bunch for the last while, as it's the intersection of a bunch of my interests. Drop me a line if you want to chat more about it sometime.
"Heck, if nothing else, having to cart around 150-180lbs of meat with fairly tall packaging and some weird requirements about having to see and what kind of forces you can exert on it is a bit of a drag to making the quickest possible wheeled vehicle."
I think I should have gone into more detail, as I believe that the statement from the article refers to entering a computer controlled car into a race with humans.
The interaction is serious important, and knowing the fastest line doesn't mean your car will get to take the fastest line if someone is already there. Pitting is going to be interesting.
Some race series, NASCAR, only allow for a limited group of sensors. I would guess something like F-1 or Indy would be easier since they have more allowed telemetry. I would imagine a NASCAR driver robot is going to have a tough time telling the pit what exactly is wrong.
That in racing games the ideal line is the same for all car, much less for all suspension tuning, speaks to me of how utterly simplistic our models are for the best way to throw a car around the track.
The ability to reliably max out your traction circle is good, very last microsecond braking wonderful, and computers are wonderful at this micro-task. But knowing when exceeding it is ok, having a feel for where you need to be, and more than anything the planning and knowledge to max out turn exit speed velocity at all costs, these are all awfully complicated & sophisticated problems that have less to do with micro-optimizing the immediate present.
I certainly think cars will get there too. But I'd be surprised if we were really modelling the right problems at this stage. Emulating a person's sense of agency and purposefulness is, my guess would be, still a stretch. A person's ability to plan better is, my guess would be, trump enough over the micro-cheats of the machine.
It'll be interesting to see the domains where AI surpasses humans quicker. You mention the "no longer has to be a car advantage," which certainly holds true in the military drone regime, but where will in evened parity computerized elegance shine through brightest first? I tend to think something like F1, which is about extremely sharp reactions with extremely capable vehicles will lend itself to the micro-optimizations of the virtual well. What about a GT Touring race, or a club car race? Wouldn't it be a shocker if AI ends up crushing humans blithely in a stock Miata, but throw it in a GT car and it remains only human in performance? Maybe AI responsiveness and terrain estimation ends up dominating the rally circuit before the track? Mapping the domains where AI does succeed the most handily ought give the most remarkable contrast.
The fatigue factor really makes a big difference. In go, there are 5d (99th percentile) bots now, and towards the end of an hour-long game you can really see the human flagging.
Computers can run a car at the limits of its traction circle much better than humans can, even with nothing more than an accelerometer. I've done some hacking around this, and it turns out that you can go about it fairly naïvely, code-wise, and do way better than even good amateur racers. Unfortunately, I don't have access to any top F1 drivers to datalog them side by side with my software, but maybe someday.
In terms of the quickest path around a given course, computers are _way_ better than humans at that. There's the official "racing line", which is a generally accepted fastest line, but as you can create more acceleration, the line actually flattens out towards the shortest route through a given section. Take a look at something like Spec Miata vs an AWD time attack car for a great demonstration of this.
Humans still have the edge on understanding & interpreting the environment. There's a lot of work being done in this area, though - I suspect we'll get some fairly robust stuff that's available to us on the open market in a few years.
Then there's the fatigue factor.
Heck, if nothing else, having to cart around 150-180lbs of meat with fairly tall packaging and some weird requirements about having to see and what kind of forces you can exert on it is a bit of a drag to making the quickest possible wheeled vehicle.
Sorry for the wall of text - this is something I've been hacking around a bunch for the last while, as it's the intersection of a bunch of my interests. Drop me a line if you want to chat more about it sometime.