Or, on seeing an anomalous <children's toy> enter the road you can probably guess that a child might follow shortly after.
I'm not suggesting that the problem cannot be solved without AGI, but you can see why some people might think that though, right?
My personal feeling is that we shouldn't be setting the bar at making a car that can handle any situation anywhere way better than any human at any time, but that we should also try to make roads that are more suitable for self driving vehicles. I'd rather we move to driving agents that don't get bored, frustrated, or angry.
And your example isn't moving goalposts, its just another legitimate example of a situation thats gotta get figured out. If you think that things like understanding that some kid learning to skateboard nearby could fall a surprisingly far distance and thus you should exercise caution, or being aware of factors that imply fast biking children (say, an adult and a child implies the potential for another fast moving child on the same trajectory), that this sort of situational and contextual awareness is critical for proper driving.. then yeah, that would be a reasonable sounding argument to support "I think self driving cars will require some level of progress in AGI".
That's all I'm long-windedly getting at.
It would be great to see the dawn of AGI, but I don't think it will ever happen with classical computation. GPT-3 spits out nonsense with the input of the largest and easiest to parse portion of reality, and I have not seen any ML approach replicate the abilities of something as simple as bacteria. ML requires constant validation from human operators, so the same is going to hold true for ML powered vehicle navigation.