This is a great point. Reminds me of how airplanes have redundant flight computers and compare the outputs of the computers to determine if one might be faulty. https://en.wikipedia.org/wiki/Fly-by-wire#Redundancy The output of different self driving models could be compared to handle more difficult driving situations -- I never thought of that.
This reminds me of ensemble. Most machine learning techniques benefit in accuracy and precision by ensembling different methods. More different the methods are (variance) better improvement you get.