[czr]

good start op. a good next step is add in temporal context (previous frame information) so model may resolve ambiguous cases. see e.g. breakdown of comma.ai openpilot model here (from their twitter, [0]), or the karpathy talk as well.

also, when presenting results, prefer to include a longer demo or sub-demos that show strengths and failure cases, and move to the top of the post rather than bottom imo. for a given reader, implementation details are either confusing and uninteresting (if not subject-matter expert) or predictable and uninteresting (if an expert, you've seen many similar before); audience for implementation details is very small number of people who want to sit down and replicate or check your work. but demos / analysis is always novel and interesting to anyone, so lead with that :)

[0] https://medium.com/@chengyao.shen/decoding-comma-ai-openpilo...

[syntaxing]

That medium link is awesome. I've been trying to find more links that dive into the innerworks of comma ai's openpilot. Do you have any more similar articles that you can recommend?

[czr]

for specifics, would recommend to simply read the code. for overall context, geohot has done interviews ([0], [1]) and that give overview of comma/openpilot at high level.

[0] http://www.autonocast.com/blog/2018/7/13/91-hotz-takes-with-... [1] https://youtu.be/iwcYp-XT7UI?t=1605

[littlemtman]

No this is the only article about the comma ai model. Many people including me,in the discord server tried to extract the weights from the snpe and carry them over to a keras model

[jacquesm]

The quality of some of the code in there is atrocious, I really hope that they don't use this for real in traffic.

[littlemtman]

It is not yet production code only ugly test code

[jacquesm]

I wasn't talking about your code, but about the comma.ai code.

[littlemtman]

Oh my bad.