  In this paper we present the first safe system for full control of
self-driving vehicles trained from human demonstrations and deployed in
challenging, real-world, urban environments. Current industry-standard
solutions use rule-based systems for planning. Although they perform reasonably
well in common scenarios, the engineering complexity renders this approach
incompatible with human-level performance. On the other hand, the performance
of machine-learned (ML) planning solutions can be improved by simply adding
more exemplar data. However, ML methods cannot offer safety guarantees and
sometimes behave unpredictably. To combat this, our approach uses a simple yet
effective rule-based fallback layer that performs sanity checks on an ML
planner's decisions (e.g. avoiding collision, assuring physical feasibility).
This allows us to leverage ML to handle complex situations while still assuring
the safety, reducing ML planner-only collisions by 95%. We train our ML planner
on 300 hours of expert driving demonstrations using imitation learning and
deploy it along with the fallback layer in downtown San Francisco, where it
takes complete control of a real vehicle and navigates a wide variety of
challenging urban driving scenarios.
