Showing posts from June, 2018

Safety Validation and Edge Case Testing for Autonomous Vehicles (Slides)

Here is a slide deck that expands upon the idea that the heavy tail ceiling is a problem for AV validation. It also explains ways to augment image sensor inputs to improve robustness. Safety Validation and Edge Case Testing for Autonomous Vehicles from Philip Koopman (If slideshare is blocked for you, try this alternate download source )

Heavy Tail Ceiling Problem for AV Testing

I enjoyed participating in the AV Benchmarking Panel hosted by Clemson ICAR last week.  Here are my slides and a preprint of my position paper on the Heavy Tail Ceiling problem for AV safety testing. Abstract Creating safe autonomous vehicles will require not only extensive training and testing against realistic operational scenarios, but also dealing with uncertainty. The real world can present many rare but dangerous events, suggesting that these systems will need to be robust when encountering novel, unforeseen situations. Generalizing from observed road data to hypothesize various classes of unusual situations will help. However, a heavy tail distribution of surprises from the real world could make it impossible to use a simplistic drive/fail/fix development process to achieve acceptable safety. Autonomous vehicles will need to be robust in handling novelty, and will additionally need a way to detect that they are encountering a surprise so that they can remain safe in the face

A Reality Check on the 94 Percent Human Error Statistic for Automated Cars

Automated cars are unlikely to get rid of all the "94% human error" mishaps that are often cited as a safety rationale. But there is certainly room for improvement compared to human drivers. Let's sort out the hype from the data. You've heard that the reason we desperately need automated cars is that 94% of crashes are due to human error, right?  And that humans make poor choices such as driving impaired, right?  Surely, then, autonomous vehicles will give us a factor of 10 or more improvement simply by not driving stupid, right? Not so fast. That's not actually what the research data says. It's important for us to set realistic expectations for this promising new technology. Probably it's more like 50%.  Let's dig deeper. ( /CC0 ) The US Department of Transportation publishes an impressive amount of data on traffic safety -- which is a good thing. And, sure enough, you can find the 94% number in DOT HS 812 115 Traffi

Can Mobileye Validate ‘True Redundancy’?

I'm quoted in this article by Junko Yoshida on Mobileye's approach to AV safety. Can Mobileye Validate ‘True Redundancy’? Intel/Mobileye’s robocars start running in Jerusalem Junko Yoshida 5/22/2018 02:01 PM EDT ... Issues include how to achieve “true redundancy” in perception, how to explain objectively what “safe” really means, and how to formulate “a consistent and complete set of safety rules” agreeable to the whole AV industry, according to Phil Koopman, professor of Carnegie Mellon University. ... Read the story here: