1. If that's the case perhaps another kind of blurring? "Intriguing properties of neural networks" (https://arxiv.org/pdf/1312.6199.pdf page 6) has examples where you get radically different classifications that I don't think would occur naturally or survive a blur with some random element, let alone two moving cameras and a sequence of images. As the title says it's an intriguing property, not necessarily a huge problem.
2. I honestly can't think of a situation where this could occur. It's the equivalent of kids shining lasers into the eyes of airline pilots, but the kids need a PhD in deep learning and specialised equipment to be able to do it. A hacker doing some update to the software via a network sounds much more plausible than attacking the system through its vision while it's traveling.
3. This is the real point in the end I guess, this Google presentation (https://www.youtube.com/watch?v=tiwVMrTLUWg) shows that the first autonomous cars to be sold will be very sophisticated with multiple systems and a lot of traditional software engineering. Hopefully LIDAR costs will come down.
2. I honestly can't think of a situation where this could occur. It's the equivalent of kids shining lasers into the eyes of airline pilots, but the kids need a PhD in deep learning and specialised equipment to be able to do it. A hacker doing some update to the software via a network sounds much more plausible than attacking the system through its vision while it's traveling.
3. This is the real point in the end I guess, this Google presentation (https://www.youtube.com/watch?v=tiwVMrTLUWg) shows that the first autonomous cars to be sold will be very sophisticated with multiple systems and a lot of traditional software engineering. Hopefully LIDAR costs will come down.