Here is a great, very accessible video where the CTO of MobileEye (which provides some of the components for Tesla&#x27;s &quot;autopilot&quot;) explains his views on the challenges of end-to-end learning for autonomous vehicles, and why it&#x27;s preferable to decompose the problem instead (still using deep learning for the decomposed modules). <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=GCMXXXmxG-I" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=GCMXXXmxG-I</a><p>I&#x27;m inclined to agree, especially because it helps in 1) providing diagnostic information (such as the great driving visualizations shown in the video), and 2) makes it easier to incorporate algorithms and sensors (like with Google&#x27;s cars) as a redundancy in case the neural network hits a crazy edge case.