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

TL;DR

DeepWORD introduces a GCN-based method for owner-member relationship detection in autonomous driving, overcoming occlusion challenges and enabling real-time, accurate perception crucial for vehicle systems.

Contribution

The paper presents a novel GCN and GAT-based approach for owner-member relationship prediction and provides a large-scale annotated dataset for benchmarking.

Findings

Achieves state-of-the-art accuracy in relationship detection.

Operates in real-time suitable for embedded systems.

Addresses occlusion issues in traffic scenarios.

Abstract

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.