I'm still not happy with self-driving on vision alone, or vision augmented with radar. There are too many hard cases for vision. Everybody who has good self-driving right now - Google, Otto, Volvo, GM - uses LIDAR. Self-driving is coming to the first end users in 2017, in Volvo's test of 100 vehicles. Volvo has multiple LIDARs, multiple radars, multiple cameras, redundant computers, and redundant actuators. They're b…
Even worse, most machine learning vision approaches make the vision problem much harder on themselves. They do not treat the visual world as the dynamic physical interacting processes that give rise to it.
A disadvantage treating of vision as static frames of pixels is that deep feedforward networks have to "memorize" all the physical dynamical effects of e.g., shadows on textures.
Such systems cannot generalize well. A more promising approach is hierarchical systems with ubiquitous recurrent connectivity.