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Real-world traffic produces more distinct scenarios than any team can pre-determine, making correct behavior in unpredictable situations the open problem of self-driving.
The central problem isn't detecting nearby objects quickly — it's handling novel situations like a burning car blocking clear lanes, where geometry permits driving through but meaning demands retreat. ✦ AI generated
Article Author · ByteByteGo Newsletter · 2026-08-17 · original ↗
A vehicle travelling at 40 miles per hour covers about 60 feet every second. Within that second, software has to determine what is physically nearby, classify each object, estimate where those objects will move, select a path, and issue steering and braking commands. Doing all of this quickly is something that has been largely solved. However, doing this correctly in unpredictable and distinct traffic situations is still an open problem. This is because real-world traffic produces more distinct scenarios than any team can pre-determine. For example, Waymo describes one such case, where a vehicle is on fire on the road ahead while the drivable lanes remain physically clear. The geometry of such a scenario might permit driving straight through it. However, the real meaning of it calls for turning around or taking preventive action.
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- ·At 40 mph, car covers 60 ft per second
- ·Software must detect, classify, predict, decide in that window
- ·Speed of processing is largely solved
- ·Correct behavior in novel situations remains open
- ·Real traffic produces more scenarios than teams can pre-determine
- ·Example: burning car blocking road, lanes physically clear
- ·Geometry permits driving through — meaning demands retreat
- ·Distinguishing physical possibility from correct action is the gap
- ·Pre-programmed responses cannot cover all edge cases
- ·Systems must infer meaning, not just geometry
- ·Open problem: handling what was never explicitly trained
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