4 accepted papers
High-definition (HD) map learning serves as an essential component of autonomous driving scene understanding, providing structured priors for planning and prediction. Recent transformer-based methods regress vectorized map elements via deformable attention over Bird's-Eye View (BEV) features. They t
High-definition (HD) map learning serves as an essential component of autonomous driving scene understanding, providing structured priors for planning and prediction. Recent transformer-based methods regress vectorized map elements via deformable attention over Bird’s-Eye View (BEV) features. They t…
High-definition (HD) maps are crucial for autonomous driving systems. Despite recent advances in learning-based HD map prediction methods, these approaches experience significant performance degradation when encountering unseen weather or lighting conditions due to feature distribution discrepancies
High-definition (HD) maps play a crucial role in autonomous driving systems. Recent methods have attempted to construct HD maps in real-time using vehicle onboard sensors. Due to the inherent limitations of onboard sensors, which include sensitivity to detection range and susceptibility to occlusion…