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Despite the numerous successes of machine learning over the past decade(image recognition, decision-making, NLP, image synthesis), self-drivingtechnology has not yet followed the same trend. In this paper, we study thehistory, composition, and development bottlenecks of the modern self-drivingstack. We argue that the slow progress is caused by approaches that require toomuch hand-engineering, an over-reliance on road testing, and high fleetdeployment costs. We observe that the classical stack has several bottlenecksthat preclude the necessary scale needed to capture the long tail of rareevents. 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 currentlyadopted state-of-the-art. This approach is based on (i) a fully differentiableAV stack trainable from human demonstrations, (ii) closed-loop data-drivenreactive simulation, and (iii) large-scale, low-cost data collections ascritical solutions towards scalability issues. We outline the generalarchitecture, survey promising works in this direction and propose keychallenges to be addressed by the community in the future.

Autonomy 2.0: Why is self-driving always 5 years away?