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

Understand

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.