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Waymo trains its cars to NOT stop at crosswalks

Wapo journalist verifies that robotaxis fail to stop for pedestrians in marked crosswalk 7 out of 10 times. Waymo admitted that it follows "social norms" rather than laws.

The reason is likely to compete with Uber, 🤦

Wapo article: https://www.washingtonpost.com/technology/2024/12/30/waymo-pedestrians-robotaxi-crosswalks/

Cross-posted from: https://mastodon.uno/users/rivoluzioneurbanamobilita/statuses/113746178244368036

93 comments
  • I work in a related field to this, so I can try to guess at what's happening behind the scenes. Initially, most companies had very complicated non-machine learning algorithms (rule-based/hand-engineered) that solved the motion planning problem, i.e. how should a car move given its surroundings and its goal. This essentially means writing what is comparable to either a bunch of if-else statements, or a sort of weighted graph search (there are other ways, of course). This works well for say 95% of cases, but becomes exponentially harder to make work for the remaining 5% of cases (think drunk driver or similar rare or unusual events).

    Solving the final 5% was where most turned to machine learning - they were already collecting driving data for training their perception and prediction models, so it's not difficult at all to just repurpose that data for motion planning.

    So when you look at the two kinds of approaches, they have quite distinct advantages over each other. Hand engineered algorithms are very good at obeying rules - if you tell it to wait at a crosswalk or obey precedence at a stop sign, it will do that no matter what. They are not, however, great at situations where there is higher uncertainty/ambiguity. For example, a pedestrian starts crossing the road outside a crosswalk and waits at the median to allow you to pass before continuing on - it's quite difficult to come up with a one size fits all rule to cover these kinds of situations. Driving is a highly interactive behaviour (lane changes, yielding to pedestrians etc), and rule based methods don't do so well with this because there is little structure to this problem. Some machine learning based methods on the other hand are quite good at handling these kinds of uncertain situations, and Waymo has invested heavily in building these up. I'm guessing they're trained with a mixture of human-data + self-play (imitation learning and reinforcement learning), so they may learn some odd/undesirable behaviors. The problem with machine learning models is that they are ultimately a strong heuristic that cannot be trusted to produce a 100% correct answer.

    I'm guessing that the way Waymo trains its motion planning model/bias in the data allows it to find some sort of exploit that makes it drive through crosswalks. Usually this kind of thing is solved by creating a hybrid system - a machine learning system underneath, with a rule based system on top as a guard rail.

    Some references:

    1. https://youtu.be/T_LkNm3oXdE
    2. https://youtu.be/RpiN3LyMLB8

    (Apologies for the very long comment, probably the longest one I've ever left)

  • The reason is likely to compete with Uber, 🤦

    A few points of clarity, as I have a family member who's pretty high up at waymo. First, they don't want to compete with uber. Waymo isn't really concerned with driverless cars that you or I would be owning/using, and they don't want (at this point anyway) to try to start a new taxi service. Right now you order an uber and a waymo car might show up. . They want the commercial side of the equation. How much would uber pay to not have to pay drivers? How much would a shipping company fork over when they can jettison the $75k-150 drivers?

    Second, I know for a fact that the upper management was pushing for the cars to drive like this. I can nearly quote said family member opining that if the cars followed all the rules of the road, they wouldn't perform well, couching it in the language of 'efficiency.' It was something like, "being polite creates confusion in other drivers. They expect you to roll through the stop sign or turn right ahead of them even if they have right of way." So now the waymo cars do the same thing. Yay, "social norms."

    A third point is that, as someone else mentioned, the cars are now trained, not 'programmed' with instructions to follow. Said family member spoke of when they switched to the machine learning model, and it was better than the highly complicated (and I'm dumbing down my description because I can't describe it well) series of if-else statements. With that training comes the issue of the folks in charge of things not knowing exactly what is going on. An issue that was described to me was their cars driving right at the edge of the lane, rather than in the center of it, and they couldn't figure out why or (at that point, anyway) how to fix it.

    As an addendum to that third point, the training data is us, quite literally. They get and/or purchase people's driving. I think at one time it was actual video, not sure now. So if 90% of drivers blast through at the moment of the red light change if they can, it's likely you'll hear about it eventually from waymo. It's a weakness that ties right into that 'social norm' thing. We're not really training safer driving by having machine drivers, we're just removing some of the human factors like fatigue or attention deficits. Again, as I get frustrated with the language of said family member (and I'm paraphrasing), 'how much do we really want to focus on low percentage occurrences? Improving the 'miles per collision' is best at the big things.'

  • Being an Alphabet subsidiary I wouldn’t expect anything less, really.

  • Pedestrians have had it too easy long enough. If elected President I will remove the sidewalks and install moats filled with alligators and sharks with loose 2x4s to cross them. Trained snipers will be watching every crosswalk so if you want a shot at making it remember to serpentine. This is Ford™ country.

93 comments