Stuff like this is completely expected of nonlinear, high dimensional controllers (and neural networks used for object detection and decision anlaysis fit into this category)...
NN's are not fit for high-predictability, high-safety-factor control. I wonder if, in fact, we cannot construct a NN controller with the level of unusual object detection and edge-case scenario recognition required to match an average human drivers capability to recognize and react appropriately to unusual situations. I mean to say that I think this is something like an NP-hard problem... to make it one-log better you need 100x the data and 100x the NN dimensionality. Model complexity explodes...
I also think we are spending a lot of time and effort solving the wrong part of the transportation problem. Cars have a thermodynamic problem for society --> a 1500kg car to move 1-2 80kg people is very energy inefficient and resource intensive to manufacturer.
We need remote work, more home delivery options (e-truck/vehicles), greater incentive for electric bikes, and better/more public transportation.
NN's are not fit for high-predictability, high-safety-factor control. I wonder if, in fact, we cannot construct a NN controller with the level of unusual object detection and edge-case scenario recognition required to match an average human drivers capability to recognize and react appropriately to unusual situations. I mean to say that I think this is something like an NP-hard problem... to make it one-log better you need 100x the data and 100x the NN dimensionality. Model complexity explodes...
I also think we are spending a lot of time and effort solving the wrong part of the transportation problem. Cars have a thermodynamic problem for society --> a 1500kg car to move 1-2 80kg people is very energy inefficient and resource intensive to manufacturer.
We need remote work, more home delivery options (e-truck/vehicles), greater incentive for electric bikes, and better/more public transportation.
my 0.02...