HIGHLIGHTS
SUMMARY
Autonomous vehicles such as self-driving cars operate in the real world, which means that the driving agent has to handle a wide range of different tasks and scenarios such as lane following, negotiating traffic and intersections, communicating (explicitly or implicitly) with pedestrians, etc. Deep learning, generally speaking, has been hugely successful in many tasks that are relevant for autonomous vehicles, most famously image processing (Geiger et_al, 2013; Wali et_al, 2015; Grigorescu et_al, 2020). Endto-end deep networks are also successful at learning how to map sensory inputs onto motor outputs for the control . . .
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