Summary: This post talks about the somewhat-unpopular topic of how Autonomous Vehicles should behave during (and directly after) unavoidable accidents, and especially how to verify that. AV accidents are clearly going to happen: Even the best driver in the world is not guaranteed to never have accidents. This is largely due to, ahem, all the … More Verifying how AVs behave during accidents
Summary: This short post talks about Mobileye’s new paper (regarding a formal approach to Autonomous Vehicles safety). It claims that the paper has several issues, but is nevertheless an important start. Mobileye came out with a paper titled “On a Formal Model of Safe and Scalable Self-driving Cars” (Bloomberg coverage, summary paper, full pdf). Their … More On Mobileye’s formal model of AV safety
Summary: I discussed before (e.g. here) how connecting rule-based verification to the rule-less, amorphous Machine Learning world is really hard, and yet necessary. The current post talks about a somewhat-exotic technique called Program Induction (PI), and how it might (eventually) help bridge that gap. What’s program induction Background: I always liked the idea of synthesizing … More Using program induction for verification
Summary: This is a short collection of some news / papers related to Autonomous Vehicles verification Some info about how Waymo does verification: The Atlantic came out with a long article about how Waymo verifies AVs. I did not see much which was new or unexpected here (but I assume they did not divulge their … More AV verification updates: Waymo, Apple and more
Summary: This is part two of my report about what I saw at the Stuttgart 2017 Autonomous Vehicles test & development symposium. It covers frameworks, simulators, scenario definitions and extracting scenarios from recordings. As I promised in part one, here is the rest of my trip report from that yearly symposium. It will cover the … More What’s new in AV verification: Stuttgart report part two
Summary: This post addresses some high-level questions like: Longer term, how much of the verification of Intelligent Autonomous Systems can be done with just Machine Learning (ML)? Should most requirements remain rule-based, and if so – how does that connect to the ML part? And how will the uneasy interface between ML and rules influence … More Where Machine Learning meets rule-based verification
Summary: This post tries to summarize what dynamic verification is, using a single picture. It then puts various verification tools, and diverse verification projects, in the context of that picture. It also explains Coverage Driven Verification (CDV). The Foretellix blog is about verifying complex systems. However, as I discussed here, there is no agreed-upon verification … More Dynamic verification in one picture
Summary: This is part one of my report about what I saw at the Stuttgart 2017 Autonomous Vehicles test & development symposium last week. This yearly symposium seems to be a pretty good place to get the feeling of what’s going on in AV verification (at least in Europe): There are several AV-related conferences, but … More What’s new in AV verification: Report from the Stuttgart symposium
Summary: This post talks about the DeepXplore paper, and uses it to revisit the topic of verification of ML-based systems The paper DeepXplore: Automated Whitebox Testing of Deep Learning Systems (by folks from Columbia U and Lehigh U) describes a new and (in my view) pretty important way to verify ML-based systems. And it somehow … More DeepXplore and new ideas for verifying ML systems
Summary: This post acts as a “table of contents” for the whole blog, linking (via tags) to the various posts. There are now quite a few posts in the Foretellix blog, and readers may get lost. So I added Tags to the various posts, and also created this “general orientation” post, which has links to … More Topics in this blog
