The examples where this happens have always seemed fairly weak to me. How many of the grave errors, not just where it&#x27;s the wrong type of animal or container but actually thinking it&#x27;s radically different, survive an application of Gaussian blur? Furthermore self-driving cars are a combination of signals; you are going to need to simultaneously fool both LIDAR and cameras.<p>On top of that you are going need to fool them over multiple frames, while the sensors get a different angle on the subject as the car moves. For example in the first Deep Q-learning paper, &quot;Playing Atari with Deep Reinforcement Learning&quot;[0], they use four frames in sequence. That was at the end of 2013.<p>I don&#x27;t think anyone will be able to come up with a serious example that fools multiple sensors over multiple frame as the sensors are moving. Even if they do then inducing an unnecessary emergency stopping situation is still not the same as getting the car to drive into a group of people. Even if fooled in some circumstances the cars will still be safer than most human drivers and still have a massive utilitarian moral case in relation to human deaths, on top of the economic case, to be used.<p>The fooling of networks is still an interesting thing, but it&#x27;s been overplayed to my mind and is not particularly more interesting than someone being fooled for a split second into thinking a hat stand with a coat and hat on it is a person when they first see it out  of the corner of their eye.<p>[0]<a href="http:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;1312.5602.pdf" rel="nofollow">http:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;1312.5602.pdf</a>  page 5