Learning how Madhu presents the idea of GTDC

Talk about formal specification of perception tasks

  • Local robustness - input domain is image domain
  • Global robustness - input domain is image domain
  • Specification w.r.t an image generator representing a reasonable set of images labelled with ground-truth. (Scenic in VerifAI)
    • Developing realistic image generator is the verification approach, not part of the specification?

Ground-truth deviation contracts is about the end-to-end analysis.

“I don’t care what I see as long as my system is safe.” - Madhu

“Even if the system acts in response to gt estimates, it preserves the invariant.” - Madhu

  • GTDC is an alternative specification for ensuring safety. If the perception satisfies GTDC, the system is safe.

  • Assuming that system with perfect perception and the model of environment is safe with given safety proof.
    • The model of environment can simulate the actual environment (simulation relation).
  • Introduce the system with actual environment and perception pipeline.
  • Mining GTDC that is likely to hold for ensuring safety.
    • We cannot ensure GTDC will always hold because of the model of the environment.
  • Visualize the wrong estimates that will lead to unsafe using the lane and lane boundary.
  • Explain the system evolves gt carefully evens through the control feedback is computed using gte.
  • GTDC links the two systems.

  • Mining GTDC can be modelled as a learning probelm with positive (gt, gte) pairs from labelled images and negative examples from the negation of invariants.