DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Driving
This addresses a specific challenge in 3D perception for autonomous driving systems, particularly in traffic jam scenarios, but is incremental as it builds on existing graph-based techniques.
The paper tackles the problem of detecting owner-member relationships between wheels and vehicles in autonomous driving, proposing DeepWORD, a GCN-based method that achieves state-of-the-art accuracy and real-time performance.
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.