I'm not an expert in the LLM space, but I'm an external contributor to comma.ai's openpilot project and I'm and quite familiar with how its controls work, so I looked from that perspective. There's two questions here:
1) Could a cloud-delivered LLM figure out how to drive this route, based on those input data and given access to those output actuators? Looks like yes. Sure.
2) Could this work in the real world? Absolutely not. Three reasons: latency, latency, and latency.
openpilot's driving model updates the target curvature and acceleration at 20Hz. Every millisecond of the round trip time through every piece of its entirely-local driving stack is well-understood, extremely consistent, and tightly optimized. It has to be, otherwise you can't react to even minor bumps or wind gusts, much less rapidly-developing traffic situations.
Adding even a single speed of light RTT to a cloud service is meaningfully bad, and you'll need a whole lot more to encode and upload camera imagery to even start the time-to-LLM-response clock, and then send the response back down. By then the world around the car has moved on.
There's a reason Tesla and every other self-driving manufacturer need the compute hardware in the car.
I'm far from neuroscience, but humans don't need to operate at 20Hz to drive a car. And human reaction latency (event to measurable action) is often over 1s (under 1Hz).
> but humans don't need to operate at 20Hz to drive a ca
This is not a helpful statement unless you can claim what speed human sensors do work at. And it's going to be faster than the latency of $(sensor + server round trip) Hertz, not getting into LLM processing time.
In humans, it's about 200–250 ms for a visual cue where you already know how to respond and you're ready, but you don't know exactly when it'll happen. It can be a fair bit longer if you need to identify what you see and choose how to respond. Typical perception to reaction time estimates for drivers when there's an unexpected hazard on the road are 1-2 seconds.
do you know what it is for non visual cue? like the gust of wind or unexpected jerk of the wheel from a road feature? both of those seem like more of a speed of proprioception which seems faster than visual and have way more to do with general "car control" in normal driving where the path is planned.
jyoung8607 · · focus · HN ↗
1) Could a cloud-delivered LLM figure out how to drive this route, based on those input data and given access to those output actuators? Looks like yes. Sure.
2) Could this work in the real world? Absolutely not. Three reasons: latency, latency, and latency.
openpilot's driving model updates the target curvature and acceleration at 20Hz. Every millisecond of the round trip time through every piece of its entirely-local driving stack is well-understood, extremely consistent, and tightly optimized. It has to be, otherwise you can't react to even minor bumps or wind gusts, much less rapidly-developing traffic situations.
Adding even a single speed of light RTT to a cloud service is meaningfully bad, and you'll need a whole lot more to encode and upload camera imagery to even start the time-to-LLM-response clock, and then send the response back down. By then the world around the car has moved on.
There's a reason Tesla and every other self-driving manufacturer need the compute hardware in the car.
ivanjermakov · · focus · HN ↗
I'm far from neuroscience, but humans don't need to operate at 20Hz to drive a car. And human reaction latency (event to measurable action) is often over 1s (under 1Hz).
bonsai_spool · · focus · HN ↗
This is not a helpful statement unless you can claim what speed human sensors do work at. And it's going to be faster than the latency of $(sensor + server round trip) Hertz, not getting into LLM processing time.
nearbuy · · focus · HN ↗
In humans, it's about 200–250 ms for a visual cue where you already know how to respond and you're ready, but you don't know exactly when it'll happen. It can be a fair bit longer if you need to identify what you see and choose how to respond. Typical perception to reaction time estimates for drivers when there's an unexpected hazard on the road are 1-2 seconds.
dgently7 · · focus · HN ↗