DeepWORD：一种基于GCN的自动驾驶车主-成员关系检测方法
DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Driving
摘要：
值得注意的是，车轮和车辆之间的所有者-成员关系对车辆的3D感知有重要贡献，特别是在嵌入式环境中。然而，目前关于上述关系预测存在两个主要挑战：i）传统的基于IoU的探试法难以处理针对遮挡的交通堵塞场景。ii）难以为车载系统建立有效的适用解决方案。为了解决这些问题，我们提出了一种创新的关系预测方法，即DeepWORD，通过设计图卷积网络（GCN）。具体来说，我们利用具有局部相关性的特征图作为节点的输入，以提高信息丰富度。此外，我们还引入了图注意力网络（GAT）来动态修正先验估计偏差。此外，我们还建立了一个名为WORD的带注释的所有者-成员关系数据集作为大规模基准，该数据集即将推出。实验表明，我们的解决方案在实践中达到了最先进的准确性和实时性。
英文摘要：
It's worth noting that the owner-member relationship between wheels and vehicles has an significant contribution to the 3D perception of vehicles, especially in the embedded environment. However, there are currently two main challenges about the above relationship prediction: i) The traditional heuristic methods based on IoU can hardly deal with the traffic jam scenarios for the occlusion. ii) It is difficult to establish an efficient applicable solution for the vehicle-mounted system. To address these issues, we propose an innovative relationship prediction method, namely DeepWORD, by designing a graph convolution network (GCN). Specifically, we utilize the feature maps with local correlation as the input of nodes to improve the information richness. Besides, we introduce the graph attention network (GAT) to dynamically amend the prior estimation deviation. Furthermore, we establish an annotated owner-member relationship dataset called WORD as a large-scale benchmark, which will be available soon. The experiments demonstrate that our solution achieves state-of-the-art accuracy and real-time in practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
