Summary: This post talks about scenarios as the main tool for serious Autonomous Vehicles verification, but mostly about the more “mechanical” obstacles standing in the way of industrial-scale usage of scenarios – those related to repeatability, HIL issues and behavior stability As promised, here is my (first) report from this year’s Stuttgart Autonomous Vehicle (AV) … More Stuttgart impressions: Scenarios and problems
Summary: This short post explains why I have not been posting, talks a bit about the upcoming Stuttgart symposium, and discusses the ecosystem I have not been blogging much lately (though I have been learning lots of new stuff). Here’s my excuse: It’s that Stuttgart time of the year again: I have been going to … More What I have been doing lately
Summary: This post discusses the influence of the Uber accident on Autonomous Vehicle (AV) deployment. It claims that AVs should eventually be deployed, and yet that we should expect many fatal AV accidents. It then suggests that a comprehensive, transparent verification system could help solve this inevitable tension. That tragic Uber accident has brought AV … More The Uber accident and the bigger picture
Summary: There are several approaches for verifying that Autonomous Vehicles are safe enough. The Pegasus project is one interesting, thoughtful attempt to do that (focusing initially on highly automated driving, not AVs). In this post I’ll summarizes a recent Pegasus symposium, and describe what I like about the approach and what is perhaps still missing. … More How to write AV scenarios (and some notes about Pegasus)
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