6 papers
HGeo-TopoMap: Boosting Topological Mapping with Hierarchical Geometric Priors
Siyu Li, Kunyu Peng, Di Wen +3
Topological maps are key outputs of autonomous driving perception systems, delivering essential road information for path planning. They identify instances such as centerlines and…
NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
Siyu Li, Fei Teng, Yihong Cao +3
Birds' Eye View (BEV) semantic segmentation is an indispensable perception task in end-to-end autonomous driving systems. Unsupervised and semi-supervised learning for BEV tasks, a…
Hallucinating 360°: Panoramic Street-View Generation via Local Scenes Diffusion and Probabilistic Prompting
Fei Teng, Kai Luo, Sheng Wu +6
Panoramic perception holds significant potential for autonomous driving, enabling vehicles to acquire a comprehensive 360° surround view in a single shot. However, autonomous driv…
CoBEVMoE: Heterogeneity-aware Feature Fusion with Dynamic Mixture-of-Experts for Collaborative Perception
Lingzhao Kong, Jiacheng Lin, Siyu Li +3
Collaborative perception aims to extend sensing coverage and improve perception accuracy by sharing information among multiple agents. However, due to differences in viewpoints and…
HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors
Siyu Li, Yihong Cao, Hao Shi +4
The exploration of Bird's-Eye View (BEV) mapping technology has driven significant innovation in visual perception technology for autonomous driving. BEV mapping models need to be…
TS-CGNet: Temporal-Spatial Fusion Meets Centerline-Guided Diffusion for BEV Mapping
Xinying Hong, Siyu Li, Kang Zeng +4
Bird's Eye View (BEV) perception technology is crucial for autonomous driving, as it generates top-down 2D maps for environment perception, navigation, and decision-making. Neverth…