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Self driving cars is based on machine learning which is basically processing massive amounts of past data. This is great for routine situations and it seems Wa

Self driving cars is based on machine learning which is basically processing massive amounts of past data. This is great for routine situations and it seems Waymo is making good progress covering most of these. However the key weakness is the lack of true intelligence. When anything unusual or unexpected happens, the best it can do is simply safely shutdown and wait for a human to intervene. And the car can't really analyze and understand its environment because that requires intelligence. It seems a best of both worlds solution would be a new type of job: Data gathering driver. It would be basically like the Google Streetview cars, but with much more sensors and input from the driver. These drivers would be assigned neighborhoods to drive through and note anything unusual or noteworthy. Stop sign is down because of last night's storm? Driver notes this and now all Waymo cars know. Street closed due to construction? All Waymo cars will avoid that street. This type of data requires intelligence to analyze and understand and it would be fed into the Waymo system for all self driving cars to benefit from it. There could be millions of self driving cars and a few thousands of these drivers feeding daily updated data to the system. You could even increase the frequency of the drivers depending on weather conditions. Maybe if there's a storm, the drivers keep driving in a loop hourly instead of once daily.

iUsedToCode 9y ago
There arent that many novel scenarios on the road. Sure, Google can't program around the possibility of an airplane falling down on you, but how often does that happen? It doesn't have to be perfect. Just very good and improving. Some time ago google shared a gif of a wheelchair chasing a duck in the middle of the road. The car didn't understand it, so it just stopped. Good enough for me. Obviously, they have a lot of work ahead of them, but don't be so pessimistic. Most people drive shitty (myself included), we aren't impossible to improve upon.
- notahacker 9y ago
  Novel scenarios might not be common compared with miles of traffic-following drudgery, but even really bad human drivers deal with novel scenarios on the road more often than they have accidents. Stopping might be a sensible safety protocol in some situations, but it isn't in others (not to mention the situations where the car may stop too late because it doesn't actually recognise that a novel scenario is about to occur). So if you want Level 5 driving, and not just a very impressive demo which is safe provided a human watches it attentively enough to take emergency measures and is able to override it when it decides it can't process a situation well enough to proceed, you need the AI to be pretty damn close to perfect in its judgement of how to react to a huge number of edge cases.
  - Retric 9y ago
    Except self driving cars are paying full attention all the time and can react significantly faster. This causes the difference in stopping distance to be dramatic. Remember, humans are basically going to do the same thing for the first 0.25 seconds in any emergency situation and that's the best case. So, self driving cars can simply be very cautious without seeming to.
    - notahacker 9y ago
      I agree that self driving cars' response time can sometimes compensate for lack of general intelligence to anticipate a visible roadside activity developing into a hazard or non-routine situations in which another driver might cut into their lane. But lightning reflexes aren't going to eliminate situations in which buggy, late or nonexistent responses to things an AI hasn't been trained to deal with endanger other road users, especially when said other road users don't have lightning reflexes themselves.
      - Retric 9y ago
        Can you give an actual example? Because, not being able to identify something is not necessarily an issue as long as the car notices something is there and it should not hit it. EX: I am sure the car had no idea what this was: https://youtu.be/Uj-rK8V-rik?t=26m11s https://youtu.be/Uj-rK8V-rik?t=26m11s but as long as it can tell it's bigger than a bread box and so it should not to hit it that's enough.
        notahacker 9y ago
        The obvious problem scenario is when unpredictable evasive action puts an autonomous vehicle into the path of other drivers (with human response times). That's when very sharp responses to an uncategorised "obstacle" that's actually a drifting plastic bag or a reflection cause more problems than they solve. Similarly, instantaneous harsh braking might help an AI save the small child it didn't anticipate might chase the football that flew past moments earlier, but a human capable of grasping that footballs are associated with pedestrians making rash decisions might have braked early and gently enough to not get bumped by the car behind. (If they didn't, they might find their late reaction blamed for the accident and possibly even get prosecuted for driving without due care and attention). The UK requires every learner driver to sit an exam consisting of identifying CGI "developing hazards" where they're scored on ability to rapidly identify stuff that might happen before they're allowed to do the full driving test. I'm sure a key focus of the teams the article discusses is teaching AI similar cases like gently slowing in the event of a football-shaped object moving near the road (which is likely far from the most difficult or obscure novelty to teach an AI to handle) but the problem space of novelties humans handle by understanding what things might be and how/if they are likely to move isn't small or one there's good reason to believe plays to AI's strengths (Meta: not sure why you're being downvoted, your contribution seems constructive and on topic to me)
flamedoge 9y ago
I can see how obtaining training data is difficult. majority of data would be non-accidents.
- dsfyu404ed 9y ago
  It's the near misses that matter because eventually you can figure out why those ones missed.
deleted 9y ago
[deleted]
adrianpike 9y ago
> Data gathering driver. Anecdotally, Uber has been gathering lots of road quality data from its drivers for years. Not sure if it still holds, but they were recording and uploading accelerometer data for a while.

iUsedToCode 9y ago

There arent that many novel scenarios on the road. Sure, Google can't program around the possibility of an airplane falling down on you, but how often does that happen? It doesn't have to be perfect. Just very good and improving. Some time ago google shared a gif of a wheelchair chasing a duck in the middle of the road. The car didn't understand it, so it just stopped. Good enough for me. Obviously, they have a lot of work ahead of them, but don't be so pessimistic. Most people drive shitty (myself included), we aren't impossible to improve upon.

notahacker 9y ago
Novel scenarios might not be common compared with miles of traffic-following drudgery, but even really bad human drivers deal with novel scenarios on the road more often than they have accidents. Stopping might be a sensible safety protocol in some situations, but it isn't in others (not to mention the situations where the car may stop too late because it doesn't actually recognise that a novel scenario is about to occur). So if you want Level 5 driving, and not just a very impressive demo which is safe provided a human watches it attentively enough to take emergency measures and is able to override it when it decides it can't process a situation well enough to proceed, you need the AI to be pretty damn close to perfect in its judgement of how to react to a huge number of edge cases.
- Retric 9y ago
  Except self driving cars are paying full attention all the time and can react significantly faster. This causes the difference in stopping distance to be dramatic. Remember, humans are basically going to do the same thing for the first 0.25 seconds in any emergency situation and that's the best case. So, self driving cars can simply be very cautious without seeming to.
  - notahacker 9y ago
    I agree that self driving cars' response time can sometimes compensate for lack of general intelligence to anticipate a visible roadside activity developing into a hazard or non-routine situations in which another driver might cut into their lane. But lightning reflexes aren't going to eliminate situations in which buggy, late or nonexistent responses to things an AI hasn't been trained to deal with endanger other road users, especially when said other road users don't have lightning reflexes themselves.
    - Retric 9y ago
      Can you give an actual example? Because, not being able to identify something is not necessarily an issue as long as the car notices something is there and it should not hit it. EX: I am sure the car had no idea what this was: https://youtu.be/Uj-rK8V-rik?t=26m11s https://youtu.be/Uj-rK8V-rik?t=26m11s but as long as it can tell it's bigger than a bread box and so it should not to hit it that's enough.
      - notahacker 9y ago
        The obvious problem scenario is when unpredictable evasive action puts an autonomous vehicle into the path of other drivers (with human response times). That's when very sharp responses to an uncategorised "obstacle" that's actually a drifting plastic bag or a reflection cause more problems than they solve. Similarly, instantaneous harsh braking might help an AI save the small child it didn't anticipate might chase the football that flew past moments earlier, but a human capable of grasping that footballs are associated with pedestrians making rash decisions might have braked early and gently enough to not get bumped by the car behind. (If they didn't, they might find their late reaction blamed for the accident and possibly even get prosecuted for driving without due care and attention). The UK requires every learner driver to sit an exam consisting of identifying CGI "developing hazards" where they're scored on ability to rapidly identify stuff that might happen before they're allowed to do the full driving test. I'm sure a key focus of the teams the article discusses is teaching AI similar cases like gently slowing in the event of a football-shaped object moving near the road (which is likely far from the most difficult or obscure novelty to teach an AI to handle) but the problem space of novelties humans handle by understanding what things might be and how/if they are likely to move isn't small or one there's good reason to believe plays to AI's strengths (Meta: not sure why you're being downvoted, your contribution seems constructive and on topic to me)

flamedoge 9y ago

I can see how obtaining training data is difficult. majority of data would be non-accidents.

dsfyu404ed 9y ago
It's the near misses that matter because eventually you can figure out why those ones missed.

deleted 9y ago

[deleted]

adrianpike 9y ago

> Data gathering driver. Anecdotally, Uber has been gathering lots of road quality data from its drivers for years. Not sure if it still holds, but they were recording and uploading accelerometer data for a while.