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

SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies