Children playing in the street are a common occurrence in residential areas, I see no reason why you would not develop a set of rules and heuristics to handle them. Identifying a pedestrian as child, and knowing whether it is running or playing, is well within the capabilities of modern computer vision.
So ... a theory of mind.
Is this type of crowdsourced driving data a crucial part of achieving Level 4+ self-driving? If so, it seems that driving around the same six city blocks of SF or cruising down Central Expressway in MV is going to produce diminishing returns in terms of producing measurable progress.
I don't think such a system would catch a false negative like the above, where the human would slow down cautiously but the self-driving system would do nothing. That situation is indistinguishable from a human slowing down to read house numbers.
To realize the problem, the system would need a full model of "what would the car be doing if not for the human input" in order to find a later point of alarming divergence.
If the kid appears in the road from behind the truck, the computer can handle slamming on the brakes very easily - probably faster than a human can - without understanding the kid any better than a group of LIDAR points or a rectangle of pixels labelled 'obstacle' by a neural network.
But if you want to brake before the kid appears from behind the truck? For that, a fully attentive human driver will be making a bunch of estimates about what the kid is doing, whether they seemed to have noticed the car, how old they were, and so on. In other words, applying a theory of mind.
Needless to say, if the latter is a must-have feature, that's a pretty hairy problem.
Of course, it's possible the decrease in deaths from being fast on the brakes in simple situations will outweigh the increase in deaths from lacking a theory of mind in complex situations. If that's the case, the self-driving car programmer's job would be a good deal simpler!