发表机构
University of California, San Diego; West Virginia University（加利福尼亚大学圣迭戈分校; 西弗吉尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出 G-MARK 框架，通过知识图谱实现协同驾驶多智能体推理，提升遮挡推理与控制选择性能，减小通信负载，效果优于现有基线。
AI 中文摘要
自动驾驶系统必须在部分可观测性下运行，安全关键对象可能被遮挡或仅对相邻联网车辆可见。车对车协作可降低这种不确定性，但现有协同驾驶方法常将多智能体证据压缩为潜在特征或隐藏多模态状态，导致无法明确哪个智能体观测到每个对象、该对象是否对 ego 车辆可见，以及冲突证据如何影响下游决策。我们提出 G-MARK，一种接地多智能体推理框架，将以对象为中心的协作观测转换为显式的可溯源知识图谱（KGs）。生成的 KGs 保留对象假设及其源归属、ego 与伙伴的可见性、不确定性、冲突、空间关系及与规划相关的上下文。G-MARK 随后从这些 KGs 中导出共享特征表示，支持轻量级任务头实现对象推理、运动预测、控制选择和轨迹预测。与最先进基线相比，GMARK 将遮挡推理准确率提升 42.2%，控制选择误差降低 13.1%，轨迹规划准确率相当，但结构化通信负载小 25.6 倍。我们的代码可在此 https URL 获取。
英文摘要
Autonomous driving systems must operate under partial observability, where safety-critical objects may be occluded or visible only to neighboring connected vehicles. Vehicle-to-vehicle cooperation can reduce this uncertainty, but existing cooperative driving methods often compress multi-agent evidence into latent features or hidden multimodal states. As a result, they obscure which agent observed each object, whether the object is visible to the ego vehicle, and how conflicting evidence affects downstream decisions. We propose G-MARK, a grounded multi-agent reasoning framework that converts cooperative object-centric observations into explicit provenance-aware knowledge graphs (KGs). The resulting KGs preserve object hypotheses together with their source attribution, ego-versus-partner visibility, uncertainty, conflicts, spatial relations, and planning-relevant context. G-MARK then derives a shared feature representation from these KGs, enabling lightweight task heads to support object reasoning, motion prediction, control selection, and trajectory forecasting. Compared with the state-of-the-art baseline, GMARK improves occlusion reasoning accuracy by 42.2%, reduces control-selection error by 13.1%, and achieves comparable trajectory-planning accuracy with a 25.6x smaller structured communication payload. Our code is available at https://github.com/bhavyagupta98/g-mark.
CommentsAccepted for oral presentation at the 25th IEEE International Conference on Machine Learning and Applications (ICMLA'26)
