Software Safety for Vehicle Automation Short Course

This is a short course lecture series that runs about 5 hours total. 

 YouTube pointers:

• L100: Look Who's Driving (PBS Nova)    (Alternate for non-USA)
• L101: Intro (9 slides) -- Playlist | Single Video
• L102:Validating Machine Learning-Based Systems (13 slides) -- Playlist | Single Video
• L103:SOTIF & Edge Cases (11 slides) -- Playlist | Single Video
• L104:Implications of Removing the Human Driver (16 slides) -- Playlist | Single Video
• L105:Safety Architectures (14 slides) -- Playlist | Single Video
• L106:How Safe is Safe Enough? (11 slides) -- Playlist | Single Video
• L107:Building Trust (10 slides) -- Playlist | Single Video
• L108: Getting to Deployed + Safe (8 slides) -- Playlist | Single Video
• L109: UL 4600 Key Ideas -- Playlist | Single Video
• L120: Overview of Automated Vehicle Terminology and J3016 Levels -- Playlist | Single Video

For those who don't have access to YouTube, an alternate source for these videos is archive.org:

L101 / L102 / L103 / L104 / L105 / L106 / L107 / L108 / L109 / L120

See also my other free on-line lectures here:  

• Slides for the above lectures and alternate format for pointer to videos here:
  https://users.ece.cmu.edu/~koopman/lectures/index.html#av
• Other lectures on autonomous vehicle technology:
  http://users.ece.cmu.edu/~koopman/lectures/index.html#talks
• Course on embedded system engineering code quality, safety, security:
  http://users.ece.cmu.edu/~koopman/lectures/index.html#642 

Edge Cases and Autonomous Vehicle Safety -- SSS 2019 Keynote

Here is my keynote talk for SSS 2019 in Bristol UK.

Edge Cases and Autonomous Vehicle Safety

Making self-driving cars safe will require a combination of techniques. ISO 26262 and the draft SOTIF standards will help with vehicle control and trajectory stages of the autonomy pipeline. Planning might be made safe using a doer/checker architectural pattern that uses  deterministic safety envelope enforcement of non-deterministic planning algorithms. Machine-learning based perception validation will be more problematic. We discuss the issue of perception edge cases, including the potentially heavy-tail distribution of object types and brittleness to slight variations in images. Our Hologram tool injects modest amounts of noise to cause perception failures, identifying brittle aspects of perception algorithms. More importantly, in practice it is able to identify context-dependent perception failures (e.g., false negatives) in unlabeled video.

• Downloadable slides
• Slideshare (also see below for embedded slideshare)

Edge Cases and Autonomous Vehicle Safety -- SSS 2019 from Philip Koopman