The bitter lesson is finally coming for the self-driving cars. The vision stack, 3D maps, lane selection grammar, occupancy networks, it’s maybe all about to give way to a single GPT looking at camera feeds and predicting the next steering wheel adjustment.
It’s mostly a latency problem at this point. The models are too big to run locally, but given that open-weight models like Qwen already exist, an open-weight, low latency equivalent to Astra can’t be too far out.
You might be interested to learn that the bitter lesson has already been grok'd by generations of autonomous car company engineers, and many or all have incorporated learned components (at minimum) in all their vehicle stacks.
There's also a very tangible limitation of the bitter lesson.
If, over time, compute climbs, and so compute-bound data-driven general architectures beat bespoke architectures (this is the bitter lesson), then it is not necessarily true that the most general architecture now beats all available bespoke architectures now (or even in the near/mid future - the crossover point is "eventually").
Bitter lesson is most tangible for long-running research directions. Sometimes you need something working as best as possible now.
Yeah. Every major self driving model that I’m aware of is fully e2e at this point. Going from fused sensor output to control+debug vectors.
This is more generalised.
But also since there’s a huge volume of data it’s too expensive to just keep scaling compute up (per car overhead) so there are necessary tricks involved.
I do think having a large model that can do this means that a small specialised model could be distilled form it though. Which is probably the most feasible path to production IMO.
valine · · focus · HN ↗
It’s mostly a latency problem at this point. The models are too big to run locally, but given that open-weight models like Qwen already exist, an open-weight, low latency equivalent to Astra can’t be too far out.
jvanderbot · · focus · HN ↗
There's also a very tangible limitation of the bitter lesson.
If, over time, compute climbs, and so compute-bound data-driven general architectures beat bespoke architectures (this is the bitter lesson), then it is not necessarily true that the most general architecture now beats all available bespoke architectures now (or even in the near/mid future - the crossover point is "eventually").
Bitter lesson is most tangible for long-running research directions. Sometimes you need something working as best as possible now.
AlphaSite · · focus · HN ↗
This is more generalised.
But also since there’s a huge volume of data it’s too expensive to just keep scaling compute up (per car overhead) so there are necessary tricks involved.
I do think having a large model that can do this means that a small specialised model could be distilled form it though. Which is probably the most feasible path to production IMO.
runsWphotons · · focus · HN ↗