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"The average US city gets 26 inches of snow per year." http://www.bestplaces.net/climate/city/california/mountain_view http://www.bestplaces.net/climate/city/c

• emmett 10y ago
  Do you really think it hasn't occurred to WayMo/Google that maybe they should test their cars in snow/rain/sleet/fog/confetti?
  • revelation 10y ago
    I'm sure it has. I'm not sure they have.
  • bambax 10y ago
    Well, they only tested in US cities, so it's unclear what has occurred to them. How will it work in small crowded streets in India, China, or just Italy?
    • DrScump 10y ago
      Or Boston?
    • IshKebab 10y ago
      I agree it is unlikely to work on non-US roads for a while but I can't really blame them for starting in the easiest conditions. It doesn't have to work everywhere at the outset to be useful.
    • drakonandor 10y ago
      Who says it has to?
    • thebspatrol 10y ago
      They are a US company, after all, who generally aims to release their innovative products and services in the US first, and the US has comparatively predictable and routine driving compared to other major countries. Why not walk before running? Especially when this is a technology that is heavily driven by machine learning?
• arjie 10y ago
  Truth be told, two thirds of America is worthless in a consumer sense. You can be successful targeting literally only the Bay Area. Why bother solving every problem at once?
• cr0sh 10y ago
  I have my doubts that any of the current systems out there - even Google's - work well in extreme inclement weather. I doubt many work well in even "normal" inclement weather (ie - light rain or snow). The biggest problem is sensors. I am not a self-driving car expert (software or hardware). Nor am I an expert in ML, computer vision, etc. But I do have minor experience in each of those. Currently, I am enrolled and taking Udacity's new "Self-Driving Car Engineer" nanodegree course. For my cohort (November), we just completed the first basic project - Lane Finding. This project utilized Python and OpenCV, and was far from a robust enough system for it to be used on a real vehicle (for one thing, it wasn't fast enough - it does well with non-real time video and still photos, but it would likely need to be re-written in C/C++ for it to stand a chance in a real-time situation). It couldn't deal with curves, and it has other corner cases. We had several still pictures to work with, then two required videos, and one bonus "challenge" (ie - extra credit) video. If we had time, we could try our hand with our own videos, etc. It was very easy to get things working with the still images, and the two regular videos. But the challenge video was something else entirely. In that video, you had to deal with not only different color lines, but also a section of different color roadway, shadows and varying light conditions. With enough effort and thought, it was easy enough to get things working properly, but it highlighted the fact that humans do some amazing things when they drive. For one thing, our eyes are better capable to handle subtle differences in color, shadow, and lighting (particularly brightness and contrast) than most traditional image sensors. Furthermore, we can "fill in gaps" and "infer" where and how things should be, based on other information in the environment. In cases of rain or snow, we can - for instance - watch where other cars go, follow the "tracks" other cars make in front of us, follow road "edges", and use other subtle cues to help us guide our vehicles as we drive (another instance - recently they repaved the road near my work - but haven't painted new lane markings - I and probably others instead used the "seams" between the asphault runs as "lane markers" instead). These and many other issues likely are "edge cases" that haven't been fully explored in this relatively new cycle in self-driving car engineering. I am confident that the problems will be solved, likely with deep learning systems of varying kinds, as well as better sensors. Sensor data fusion and priority could also help (ie - use a LIDAR to "follow" a car in front of you, perhaps? Of course, filter out the noise of the falling rain/snow, first). So far, this course has enlightened me more on just how hard of a problem overall self-driving vehicle systems are to solve. Furthermore, there aren't many players (companies nor individuals) in the market (which is why I am taking the course - to expand on my current skills). Solving these kinds of hard problems will be paramount for a successful self-driving vehicle to last in the market.
• bb611 10y ago
  Everyone working on autonomous vehicles is facing a similar issue, and it's also one that human drivers are pretty bad at. Most people solve it by not going outside as frequently with snow on the roads. Tesla's goal is high fidelity GPS mapping of roads and their landmarks, so that the car can navigate down a lane in the absence of normal visual signals (i.e. clear road markings). It will be interesting to see how everyone else deals with weather conditions, but it's also not a problem that needs to be solved instantly.
  • walrus1066 10y ago
    The weather problem needs to be solved if you want fully autonomous vehicles with no human on standby, otherwise when it snows you'd have a bunch of autonomous vehicles stood still, blocking the road not knowing what to do.
    • bb611 10y ago
      There are tens of millions of US customers who live in areas that never receive snow and rarely receive rain. It's clear there are a few different technical solutions, and probably the engineers working on these problems will think of a few more in the next decade, if it's a major challenge SDCs will just be targeted at minimal weather markets to start out.