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.
> The bitter lesson is finally coming for the self-driving cars.
Maybe, but the opacity level of models is not acceptable for cars. "Why did it drive under the semi?" "Model said to." "Why did the model say to?" "shrug"
But if the model is an LLM, you actually COULD ask it why it drove under the semi, and it would give you an answer. Now, you may argue that it will just be generating a whole new, backwards-rationalized post-hoc explanation of its own behavior given the logs that it managed to take before the crash. But then I ask you: how do you think a person explains why they did what they did after a crash? I direct you to all of the unsettling split-brain neuroscience literature demonstrating that humans are incorrigible backwards rationalizers who make for unreliable witnesses.
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.
JoshTriplett · · focus · HN ↗
Maybe, but the opacity level of models is not acceptable for cars. "Why did it drive under the semi?" "Model said to." "Why did the model say to?" "shrug"
sebastos · · focus · HN ↗
JoshTriplett · · focus · HN ↗