G-MARK: Grounded Multi-Agent Reasoning for Cooperative Driving via Knowledge Graphs
Score
77将可溯源知识图谱引入协同驾驶，明确解决观测来源与冲突证据的可解释性问题，较现有黑盒特征融合有明显增量。
方法设计完整、对比实验充分，并提供开源实现，技术论证扎实。
在遮挡场景精度与通信负载上带来显著改善，适合车联网协同驾驶落地参考。
发表于 arXiv 最新收录论文，回应协同感知可解释性的当前热点。
来源为 arXiv 预印本，尚未经过同行评审，但提供可复现代码与强基线对比。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper introduces a novel method with open-source code and reports substantial gains on key metrics while emphasizing interpretable multi-agent reasoning.
AI commentary
「What stands out here is the shift from compressed latent features to explicit provenance-aware knowledge, which lets the system answer who saw what—critical for safety-critical autonomous driving. It is especially useful for researchers working on V2X cooperative perception, occlusion reasoning, or interpretable AI.」
Quick overview
Autonomous driving systems operate under partial observability, where safety-critical objects may be occluded or visible only to neighboring connected vehicles. Existing cooperative driving methods often compress multi-agent evidence into latent features, obscuring source attribution and conflicting evidence. G-MARK converts cooperative object-centric observations into provenance-aware knowledge graphs that preserve object hypotheses with source attribution, ego-versus-partner visibility, uncertainty, conflicts, spatial relations, and planning context. Lightweight task heads built on the shared KG representation support object reasoning, motion prediction, control selection, and trajectory forecasting. Compared with the state-of-the-art baseline, G-MARK 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.
- Proposes an explicit, interpretable cooperative driving reasoning framework that converts multi-vehicle observations into provenance-aware knowledge graphs.
- Preserves occlusion, conflict, and uncertainty information to support object reasoning, motion prediction, control selection, and trajectory forecasting.
- Improves occlusion reasoning accuracy by 42.2% and reduces control-selection error by 13.1% over the SOTA baseline.
- Reduces structured communication payload by 25.6x while maintaining comparable trajectory-planning accuracy.
Full text
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