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Even the best self-driving cars keep getting confused and crashing into stuff, sometimes with fatal results.
Here’s a thought: maybe the simulations being used to test them aren’t wacky enough.
A team of researchers have unveiled a new benchmark for testing autonomous vehicles that subjects them to all sorts of “unseen” and truly random scenarios — such as an elephant lumbering across a city street.
“Why did the elephant cross the road? To expose how fragile your model is,” Andreas Geiger, head of the Autonomous Vision Group at the University of Tübingen in Germany, and coauthor of a new preprint paper, wrote in a LinkedIn post.
In footage provided by the researchers, a simulated AV veers into one of these gentle giants, mowing it down. In another test, the car stops in front of — and then slams into, for some reason — a playground slide sitting in the middle of the road. The car also gets foiled by a Looney Tunes-style wall painted to look like the road ahead (a ruse that’s confounded real-life self-driving cars.)
Why did the elephant cross the road? To expose how fragile your model is.
There's a relatively quiet but serious problem in autonomous driving research: most models are trained and evaluated on the same scenarios. pic.twitter.com/eAXHiZTZ1U — Katrin Renz (@KatrinRenz) April 23, 2026
Without the added context, you could mistake these for clips of a hacker trolling players in “GTA Online.” But they serve a serious purpose, according to Geiger.
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