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Comment by dchichkov | Hacker News Reader

One example that I like to give is a downed power line. This is something that happens often. Try solving it with rigorous physics.

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A purely physical approach is just a design paradigm in how to use other sensors (with or without ML), having this means uncertainty would be correlated with momentum and anything causing doubt (a power line down) would mean reducing the internal energy of the system to give ability to either stop to a halt, enter a safe configuration or assess the situation deeper/differently to decide.

So far what we see about Tesla and the likes is that they jumped early on the ML fad as very naive feedback loops on basic car controls (estimate lanes -> center the car. estimate obstacle distance -> adapt speed). It's not physics first.

The problem is, you'd be hitting all these pesky corner cases, with some of them requiring active avoidance - downed power lines, water on the road, vehicles on fire, other traffic participants. My guess, it is hard to engineer a system that could handle the long tail of making decisions under uncertainty with "purely physical approach". You'll run out of complexity budget.

it's certainly full of dimensions. I may be a ideal-extremist here, and business probably doesn't like being overly cautious, but at least that would be a vehicle I'd trust no to endanger anybody (in or out), even if it looks underperforming compared to autopilot~.

Well, usually driving over one with rubber tires causes no problem. One in the air would be detectable, no?

It is hard to even recognize it. On the road it looks exactly like a crack. For a case of a low hanging one - it is invisible by LIDAR or radar, on the camera the difference between a regular one and a low hanging one is very subtle.

And the requirement is not just braking action, like with a railroad crossing it is active avoidance.

https://www.pge.com/en_US/safety/electrical-safety/what-to-d...

My point is - corner cases like these tend to create "IF" statements in a "pure physics model" that is used for decision making under uncertainty. And engineering such models without "ML fad" is difficult.

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