• svtdragon@lemmy.world
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    21 days ago

    According to some cursory research (read: Google), obstacle avoidance uses ML to identify objects, and uses those identities to predict their behavior. That stage leaves room for the same unpredictability, doesn’t it? Say you only have 51% confidence that a “thing” is a pedestrian walking a bike, 49% that it’s a bike on the move. The former has right of way and the latter doesn’t. Or even 70/30. 90/10.

    There’s some level where you have to set the confidence threshold to choose a course of action and you’ll be subject to some ML-derived unpredictability as confidence fluctuates around it… right?

    • tibi@lemmy.world
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      21 days ago

      In such situations, the car should take the safest action and assume it’s a pedestrian.

      • svtdragon@lemmy.world
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        20 days ago

        But mechanically that’s just moving the confidence threshold to 100% which is not achievable as far as I can tell. It quickly reduces to “all objects are pedestrians” which halts traffic.