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It seems that Tesla’s main issue is detection, not decision making (well at least their biggest issue). Sure, some environments like extreme weather are not be
by upbeat_general 5y ago
It seems that Tesla’s main issue is detection, not decision making (well at least their biggest issue).
Sure, some environments like extreme weather are not being entered by most self-driving companies. However, their detection stacks in general work fine in the vast majority of places, it’s their decision making that needs work.
As others pointed out, Uber failed not because of a lack of detection of the person. Moreover, the cases where Tesla publicly fails aren’t particularly challenging for detection (daylight, good weather).
- XorNot 5y agoPeople treat this as though all Tesla's everywhere were for some reason never trained on a dataset including cyclists off to the side, and people make emotional statements of "obviously this is someone's fault for not dealing with this obvious scenario". But we all (should) know that's not how machine object detection systems work: they would reliably pass this sort of scenario all the time, and then some sort of adverse (input data wise) input situation from something humans don't even notice would trigger a complete failure. Which is to say it's not intuitively obvious that other systems won't have these sorts of failure scenarios - it certainly won't be proven till they have sufficiently widespread deployment to eek them out.
- upbeat_general 5y agoTotally agree here. I do think redundant detection is important though and highlights the issue with the vision-only approach. If your object detection fails unexpectedly, as often happens with our current approaches, it's not catastrophic if you have another sensor modality to at least indicate there's a serious missed detection.