Self-driving cars: Near-miss driving data can expedite AV algorithm training - Tech Xplore

Ah, the University of Michigan has discovered that training autonomous vehicles on 'near-miss' data boosts algorithm safety by 90%. Because nothing says rigorous safety testing like learning from the times you almost crashed. It's a bit like a pilot training exclusively on near-stall recoveries and being declared 90% safer—assuming you never actually stall. The simulation data, of course, is pristine: no pedestrians with unpredictable jaywalking habits, no cyclists with death wishes. Just clean, sanitized almost-accidents that make for great statistics.

But let's not spoil the party with reality. The 90% improvement is presumably measured against some baseline that the researchers chose, and we all know how generous baselines can be. The real test—driving on actual roads with actual humans—will reveal whether these near-miss-trained algorithms can handle the glorious chaos of real-world traffic. My money is on 'most likely not, but we'll get another funding round out of it.'

Still, credit where due: the folks at UM have found a clever way to wring value out of data that would otherwise be discarded. We'll believe the self-driving revolution is here when we see a single AV navigate a school run without a chaperone. Until then, pass the popcorn.