Leveraging V2X for Collaborative HD Maps Construction Using Scene Graph Generation
This addresses the need for real-time, cost-effective HD maps in autonomous driving, though it appears incremental as it builds on existing V2X and graph-based methods.
The paper tackles the problem of costly and non-real-time HD map generation for autonomous vehicles by proposing HDMapLaneNet, a framework that uses V2X communication and Scene Graph Generation to collaboratively construct maps, achieving superior association prediction performance on the nuScenes dataset.
High-Definition (HD) maps play a crucial role in autonomous vehicle navigation, complementing onboard perception sensors for improved accuracy and safety. Traditional HD map generation relies on dedicated mapping vehicles, which are costly and fail to capture real-time infrastructure changes. This paper presents HDMapLaneNet, a novel framework leveraging V2X communication and Scene Graph Generation to collaboratively construct a localized geometric layer of HD maps. The approach extracts lane centerlines from front-facing camera images, represents them as graphs, and transmits the data for global aggregation to the cloud via V2X. Preliminary results on the nuScenes dataset demonstrate superior association prediction performance compared to a state-of-the-art method.