ImagineMap: Enhanced HD Map Construction with SD Maps
This work addresses a domain-specific problem in autonomous driving by incrementally improving HD map construction through better detection accuracy.
The paper tackles the problem of constructing high-definition (HD) maps from multi-view images and standard-definition (SD) maps by proposing a novel architecture that integrates SD map priors to improve lane line and area detection, resulting in enhanced performance for downstream topology tasks.
Track Mapless demands models to process multi-view images and Standard-Definition (SD) maps, outputting lane and traffic element perceptions along with their topological relationships. We propose a novel architecture that integrates SD map priors to improve lane line and area detection performance. Inspired by TopoMLP, our model employs a two-stage structure: perception and reasoning. The downstream topology head uses the output from the upstream detection head, meaning accuracy improvements in detection significantly boost downstream performance.