SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving
For autonomous driving under occlusion and latency constraints, SwarmDrive demonstrates that semantic edge cooperation with SLMs can improve performance, but the evaluation is limited to a single simulated intersection case.
SwarmDrive uses local Small Language Models on vehicles to share compact intent distributions via V2V coordination, improving success rate from 68.9% to 94.1% over a single local SLM and reducing latency from 510 ms to 151.4 ms in an occluded intersection scenario.
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.