Comment by jayjay71 | Hacker News Reader

Comment by jayjay71 | Hacker News Reader

Parent Full thread

I think it actually doesn't make that much difference. The problem, I believe, is that people are used to rapid advances in software. Lots of small iterations can take you a long way, but sometimes you just reach a hurdle which requires a breakthrough. A fully self-driving car would need a huge breakthrough in perception (which most people categorize under AI) in order to work.

Computer vision actually started out in the 60's because how hard could it be to classify objects in images? Well, decades later and it's still a hugely unsolved problem.

Robotics can be broken into 3 main categories: perception, planning, and controls. Perception is always the weak point. Robots just cannot see and understand their environments like people can. When you put a robot in a dynamic environment, they fail spectacularly. To give some idea of this, look at videos of robots falling down at the DARPA Robotics Challenge - and keep in mind those were made by teams of top roboticists with millions of dollars in a highly constrained environment (it's hard to say that obstacle course was "real").

I really, really doubt it's going to happen in a few years. I would love to be wrong though.

Last time I checked Google's self driving car was doing pretty well already without major breakthroughs. I suppose there still are problems when the weather conditions are less than ideal, but you can't compare it to /r/shittyrobots.

Many places have "less than ideal weather conditions" for 100+ days a year.

The car's performance on a relatively small stretch of road in California is hardly indicative of its potential performance on millions of miles of varied roadways, terrain and climate that make up the rest of the world.

How does it do in a snowstorm? Heavy rain?

well, hasn't google driven around actual city streets for many millions of miles? I could be wrong, but driving a car doesn't actually require breakthroughs in perception beyond current technology, The issue is that it just isn't economical for individual car ownership at this stage, but it could very well make economic sense for a fleet of taxis. Note, those taxis don't have to have the ability to go just anywhere, they could have predefined routes

> how hard could it be to classify objects in images? Well, decades later and it's still a hugely unsolved problem.

AFAIK Google's cars rely on radar to identify the position of objects, which I would think is vastly easier.

Of course, there's still the interesting challenge of classifying objects into traffic partipicants and obstacles.

>>sometimes you just reach a hurdle which requires a breakthrough.

When there are billions of $'s to be made. Generally there is way.

I understand from where you are coming. But if the trend grows, and one companies sees the billions coming. Others are forced to innovate. Also a whole new ecosystem is born.

See pre iPhone and post iPhone mobile industry. Sometimes you need one company to do something insane to drive others down the same path.

Reply on news.ycombinator.com