GPT‑6 Astra 的实时驾驶能力因延迟和缺乏开源低延迟模型而受限
问题概述
当前大型语言模型在实时车辆控制中面临高推理延迟，导致无法满足约20Hz的控制回环需求；此外，缺乏可公开获取的低延迟驾驶模型使得开发者难以在真实车辆上部署类似Astra的系统。
痛点标签
- 高推理延迟导致无法实现实时控制回环
- 缺乏开源的低延迟驾驶模型
- 视觉基准与专用系统之间的性能鸿沟
待验证问题
- 您是否在实际驾驶场景中遇到过模型延迟导致的控制问题？
- 您是否愿意尝试使用开源低延迟驾驶模型进行实验？
- 您认为当前的自动驾驶系统在多大程度上需要解决视觉基准的差距？
证据
原话
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
原话
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.
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