For me, it's more nuanced than this. Most mistakes that are made by human drivers around me make intuitive sense to me based on how they are driving. They are o

For me, it's more nuanced than this. Most mistakes that are made by human drivers around me make intuitive sense to me based on how they are driving. They are often somewhat predictable. In those cases, I can partially mitigate those ahead of time (not always - I've been broadsided where I didn't see the other car until the last moment). The wide adoption of cell phones required a significant update to my mental model, but you can spot things like lane drift, uneven speed, etc. that tell you to give space to someone because they are probably distracted. Both chemically-impaired and sleep-deprived drivers tend to give even more clues to stay clear. I find predictions informed by observation much more difficult with automated driving systems. In many cases, such cars will appear to be driving perfectly, right up until a severe mistake is made. Instead of having observable clues, it requires an understanding of what sensor suite and what version of software is in which vehicle in order to understand the tendencies of that vehicle. Something we can't possibly keep up with, especially given that the developers of the machine learning models being deployed can't deeply characterize each deployment's failure modes. Given all that, I feel more able to defend myself against human mistakes than automated mistakes.