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  • I’m morbidly curious whether the (supposedly) superior compaction support in recent GPT models with an appropriate harness has anything to do with this. A conventional LLM with conventional attention is, of course, wildly unsuitable to continuous tasks like driving, but maybe as the technology advances it will improve in its ability to sort-of work.
    by amluto
  • Surprised they didn’t try Qwen’s recently open sourced driving model https://huggingface.co/Qwen/Qwen-Drive-1.0-4B
    by syntaxing
  • Looking forward to the juggling bananas benchmark. If Claude can only manage 5 and Astra does 6, clearly they have a better model.
    by 1970-01-01
  • Has anyone else noticed human drivers becoming more aggressive and causing more accidents than ever?

    I thought it was because my smaller town was overrun after COVID by transplants, but I'm hearing similar complaints from other places I was considering relocating to.

    Perhaps the solution will be robocars where, if there's a potential road rage scenario, the passengers can duke it out in a VR headset session.

    by sys32768
  • Wow! but WHY is this a benchmark?? for comparison tesla's model is approximately 10-15B parameter model (estimating from maxxing the hardware that comes with the car at 16gb ram).
    by prometheus1992
  • 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.

    by valine
  • What did they do to Astra so cracked at vision (and computer use). That ARC 3 score turned out to be no joke/fluke. That huge gap between Astra and Fable (in this case) is basically every hard vison/spatial benchmark i've seen including non-benchmarks like playing games (Portal, Factorio, RimWorld).

    SpatialBench - https://x.com/spicey_lemonade/status/2096365630190698516

    ZeroBench - https://zerobench.github.io/

    Robot Arms - https://openai.robocurve.org/gpt-6-astra/

    by famouswaffles
  • 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.

    by jyoung8607

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