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It's worth noting that the owner-member relationship between wheels andvehicles has an significant contribution to the 3D perception of vehicles,especially in the embedded environment. However, there are currently two mainchallenges about the above relationship prediction: i) The traditionalheuristic methods based on IoU can hardly deal with the traffic jam scenariosfor the occlusion. ii) It is difficult to establish an efficient applicablesolution for the vehicle-mounted system. To address these issues, we propose aninnovative relationship prediction method, namely DeepWORD, by designing agraph convolution network (GCN). Specifically, we utilize the feature maps withlocal correlation as the input of nodes to improve the information richness.Besides, we introduce the graph attention network (GAT) to dynamically amendthe prior estimation deviation. Furthermore, we establish an annotatedowner-member relationship dataset called WORD as a large-scale benchmark, whichwill be available soon. The experiments demonstrate that our solution achievesstate-of-the-art accuracy and real-time in practice.

DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Driving