I'm not sure why your comment seems to be grey-ed out. Here's the relevant section from the report:
"As the vehicle and pedestrian paths converged, the self-driving system software classified
the pedestrian
as an unknown object, as
a vehicle,
and then as
a bicycle
with varying expectations of future
travel path."
1. It seems like the classifier flipped state between pedestrian/unknown object/vehicle/bicycle; this seems like one of the well-known issues with machine learning. (I'm assuming the classifier is using ML simply because I have never heard of any other (semi-?) successful work on that problem.)
I suggest that the problem is that the rest of the driving system went from 100% certainty of A to 100% certainty of B, etc., with a resulting complete recalculation of what do to about the current classification. I make this hypothesis on the basis of the 4+ seconds when the car did nothing, while a response to any of the individual possibilities would possibly have averted the accident.
2. If the classifier was flipping state, I assume the system interrupted the Decide-Act phases of an OODA loop, resulting in the car continuing its given path rather than executing any actions. This seems like a reasonable thing to do, if the system contains no moment-to-moment state. Which would be strange; it seems like the planning system should have some case for having obstacles A, B, C, and D rapidly and successively appearing in the same area of its path.
3. Assuming the classifier wasn't flipping state, but presenting multiple options with probabilities, I can see no reason why the car wouldn't have taken some action in the 4+ seconds. (I note that the trajectory of the vehicle seems to move towards the right of its lane, which is a rather inadequate response and likely the wrong thing to do for several of the classification options.)
"According to Uber, emergency braking maneuvers are
not enabled while the vehicle is under computer control, to
reduce the potential for erratic
vehicle
behavior."
That's just idiotic and would be nigh-criminally unprofessional in most engineering situations.
"As the vehicle and pedestrian paths converged, the self-driving system software classified the pedestrian as an unknown object, as a vehicle, and then as a bicycle with varying expectations of future travel path."
1. It seems like the classifier flipped state between pedestrian/unknown object/vehicle/bicycle; this seems like one of the well-known issues with machine learning. (I'm assuming the classifier is using ML simply because I have never heard of any other (semi-?) successful work on that problem.)
I suggest that the problem is that the rest of the driving system went from 100% certainty of A to 100% certainty of B, etc., with a resulting complete recalculation of what do to about the current classification. I make this hypothesis on the basis of the 4+ seconds when the car did nothing, while a response to any of the individual possibilities would possibly have averted the accident.
2. If the classifier was flipping state, I assume the system interrupted the Decide-Act phases of an OODA loop, resulting in the car continuing its given path rather than executing any actions. This seems like a reasonable thing to do, if the system contains no moment-to-moment state. Which would be strange; it seems like the planning system should have some case for having obstacles A, B, C, and D rapidly and successively appearing in the same area of its path.
3. Assuming the classifier wasn't flipping state, but presenting multiple options with probabilities, I can see no reason why the car wouldn't have taken some action in the 4+ seconds. (I note that the trajectory of the vehicle seems to move towards the right of its lane, which is a rather inadequate response and likely the wrong thing to do for several of the classification options.)
"According to Uber, emergency braking maneuvers are not enabled while the vehicle is under computer control, to reduce the potential for erratic vehicle behavior."
That's just idiotic and would be nigh-criminally unprofessional in most engineering situations.