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English(EN) SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving

SwarmDrive 使用本地SLM实现合作式自动驾驶

研究人员开发了SwarmDrive,一个利用车辆本地小型语言模型(SLM)的合作式自动驾驶新框架。该系统仅在不确定性高时共享精简后的意图信息,减少了对云端LLM推理及其相关延迟和连接性问题的依赖。在复杂交叉路口场景的模拟中,SwarmDrive将成功率从68.9%提高到94.1%,同时显著降低了延迟,但同时也注意到随着车队规模增大,通信开销有所增加。 AI

影响 展示了基于边缘的LLM协同在提高自动驾驶安全性和降低延迟方面的潜力。

排序理由 详细介绍自动驾驶新框架的学术论文。

在 arXiv cs.AI 阅读 →

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SwarmDrive 使用本地SLM实现合作式自动驾驶

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详细介绍自动驾驶新框架的学术论文。
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Anjie Qiu, Donglin Wang, Zexin Fang, Sanket Partani, Hans D. Schotten ·

    SwarmDrive:面向低延迟协同的自动驾驶的语义V2V协同

    arXiv:2604.22852v1 Announce Type: cross Abstract: Cloud-hosted LLM inference for autonomous driving adds round-trip delay and depends on stable connectivity, while purely local edge models struggle under occlusion. We present SwarmDrive, a semantic Vehicle-to-Vehicle (V2V) coordi…