  Despite the numerous successes of machine learning over the past decade
(image recognition, decision-making, NLP, image synthesis), self-driving
technology has not yet followed the same trend. In this paper, we study the
history, composition, and development bottlenecks of the modern self-driving
stack. We argue that the slow progress is caused by approaches that require too
much hand-engineering, an over-reliance on road testing, and high fleet
deployment costs. We observe that the classical stack has several bottlenecks
that preclude the necessary scale needed to capture the long tail of rare
events. To resolve these problems, we outline the principles of Autonomy 2.0,
an ML-first approach to self-driving, as a viable alternative to the currently
adopted state-of-the-art. This approach is based on (i) a fully differentiable
AV stack trainable from human demonstrations, (ii) closed-loop data-driven
reactive simulation, and (iii) large-scale, low-cost data collections as
critical solutions towards scalability issues. We outline the general
architecture, survey promising works in this direction and propose key
challenges to be addressed by the community in the future.
