From d90fed72fa8a580469422ecab6ebfea3f88d1dd7 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Mon, 7 Jul 2025 13:46:42 -0400 Subject: [PATCH] vault backup: 2025-07-07 13:46:42 --- ...ety Assurance of Machine Learning Controllers.md | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/Notes on Papers/On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers.md b/Notes on Papers/On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers.md index cae275f31..43ed998b4 100644 --- a/Notes on Papers/On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers.md +++ b/Notes on Papers/On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers.md @@ -1,9 +1,20 @@ # First Pass **Category:** Experimental Results + **Context:** They used a F1/10 model of an autonomous car to test out a simplex -safety structure +safety structure on a neural network based controller. They try several +different neural net types. Their trick is they use real time reachability to tell + when to switch between an optimal controller and the simplex guard. + **Correctness:** +They seem to do things pretty well and by the book. All of their explanations +make sense and they do a good job citing sources. They do punt when it comes +to talking about the formal verification of the switching mechanism, but they +do make note that's future work. + **Contributions:** +They show how a simplex system can work and the difficulties + **Clarity:** # Second Pass