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Comment by Sanddancer | Hacker News Reader

There are standards, Toyota simply chose to ignore all of them. Standards such as MISRA [1] and DO-178C [2] exist for the purpose of ensuring software quality in safety-critical situations. Most embedded software development environments even include tooling to help verify that you're not doing the things that are unsafe. The problem lies more in that automakers aren't required to use any such standard, unlike what the FAA has required for decades.

[1] http://en.wikipedia.org/wiki/Motor_Industry_Software_Reliabi... [2] http://en.wikipedia.org/wiki/DO-178C

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Toyota couldn't ignore these (voluntary) standards given that MISRA-C nor the earlier DO-178 didn't exist at the time the code was developed.

MISRA-C dates from 1998, and DO-178B, which was the previous revision of DO-178, dates from 1992.

Yeah, I mistyped (should read "...later DO-178..."). You're quite correct that DO-178B was around then, but the C revision was not.

According to testimony, Toyota's coding standard was in place in 1997, before the first MISRA-C publication.

First, you're ignoring the words "such as".

Second, DO-178 was originally published in 1992.

Very awesome comment! I didn't know there were standards, but it's great to see there are. However, Toyota should have followed them. I mean, did they think they knew better? I think with self driving cars, the standards will become more requirements; though I doubt before a series of deaths result from software failure.

Given that a lot of computer vision work is based on randomised algorithms, do you think that these standards would be enough? You could demonstrate 100% MC/DC coverage through a neural net implementation, but the weights are where the faults probably exist, for example.

That actually leads to a very interesting point; would self driving cars be vulnerable to adversarial imagery?

Neural nets are well known for being easily fooled [1] ... I wonder if you could create similar situations for self-driving cars.

[1] http://www.evolvingai.org/fooling

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