It's said that the average car comprises some 30,000 parts, and today’s machines are irrefutably as complex as ever.
One could argue that electric cars have simpler powertrains, but, from battery chemistries to motor cooling and advanced aerodynamics, the challenges they introduce are more granular and difficult to pin down.
This is where artificial intelligence (AI) comes into play. By modelling tests in excruciating detail numerous times, machine learning can separate variables from the veritable chaos that is a car in motion, where humans would struggle to.
Richard Ahlfeld, CEO of AI firm Monolith, explained: “In my experience, if I give you five data points, you're going to see a trend and AI algorithms will not. If I give 50 data points, it's going to be much harder for you, so there’s already a little bit of value in a machine-learning tool. If I give you 1000 data points, you're going to be completely lost, because you can't think about 1000 things at the same time, but the machine-learning algorithm is happy.”
BMW partnered with Monolith in 2019 to test whether it could identify the variables that determine whether the tibia (lower leg bone) breaks in a car crash.
Traditionally, this would be nigh-on impossible to identify. “If you take a crash test at BMW, then this is obviously something really complex: there’s thousands of components hitting the wall at 60mph,” explained Ahlfeld.
“There are 1100 sensors with really high sampling rates. So it creates something really complex, where stuff is flying around – a huge amount of data. And they keep doing it. A company like BMW does thousands of those tests per year. This creates something complicated [and] really hard to understand – something really hard to model [using traditional statistical methods].”
But by running historic crash test data through Monolith, varying vehicle mass and speed, BMW was able to better understand the tibia – and the point at which it breaks in a crash – without investing in more real-world tests.
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