SwarmDrive: 语义车辆间协调用于延迟受限的协同自动驾驶
SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving
AI总结：
SwarmDrive通过语义车辆间协调框架，在高不确定性时共享紧凑意图分布，并通过事件触发共识融合，提升协同自动驾驶性能，减少延迟并提高成功率。
摘要：
云托管的大语言模型推理用于自动驾驶会引入往返延迟并依赖稳定的连接性，而纯本地边缘模型在遮挡情况下表现不佳。我们提出了SwarmDrive，一种语义车辆间（V2V）协调框架，其中附近的车辆运行本地小型语言模型（SLMs），在不确定性高时仅共享紧凑的意图分布，并通过事件触发共识融合。我们在一个遮挡交叉口案例中进行了一项5种子可执行研究，结合了匹配的操作点比较与鲁棒性扫描。在该设置下，SwarmDrive在其6G通信设置（
英文摘要：
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) coordination framework in which nearby vehicles run local Small Language Models (SLMs), share compact intent distributions only when uncertainty is high, and fuse them through event-triggered consensus. We evaluate SwarmDrive in a 5-seed executable study built around one occluded intersection case, combining matched operating-point comparisons with robustness sweeps. In that setting, SwarmDrive under its 6G communication setting ("Swarm 6G") raises success from 68.9% to 94.1% over a single local SLM while reducing latency from a 510 ms cloud reference to 151.4 ms. However, an increased number of participating vehicles leads to higher communication overhead and packet loss. SwarmDrive also evaluates the impact of swarm-size, packet-loss, and entropy-threshold sweeps and shows that the cooperative gain holds across ablations and is best balanced near an active swarm size of 4 vehicles and an entropy trigger threshold of 0.65 in the current prototype. These results show that semantic edge cooperation can work under tight latency constraints in the targeted intersection case, but they are not a deployment-grade validation of a real 6G stack.
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