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A person laying on the ground in a crosswalk was likely never considered by the team to include in their training data. Those outlier situations are exactly what real world data is needed for. And the only way to properly train for most of these situations is to drive in the real world. The real world isn't perfect situations and nice lines on fresh asphalt so while base training in perfect situations is useful, it will still miss the exact same situation in a real world environment with crappy infrastructure.
Not sure what or how Cruise uses the data collected in real-time, but I can see camera visuals categorizing a person laying in the crosswalk as something like damage to painted lines, and small debris that can be ignored. Other sensors like radar and lidar might have categorized returns as something like echoes or false results that could be ignored, again because a person laying in the crosswalk is extremely unlikely. False data returns happen all the time with things like radar and lidar, millions of data points are ignored as outliers or info that can be safely ignored, and sometimes that categorization is incorrect.
I didn't bother reading any further than this. The person was on the crosswalk when both cars started moving. Neither car should have been moving while anyone was still on the crosswalk.
Thats the whole point of their comment. The car did not recognize anyone was on the crosswalk because it was never trained to look for people laying in the crosswalk.
They are 100% trained on bodies laying prone on the ground.