5 accepted papers
Autonomous driving requires accurate scene understanding, including road geometry, traffic agents, and their semantic relationships. In online HD map generation scenarios, raster-based representations are well-suited to vision models but lack geometric precision, while graph-based representations re
Online mapping and end-to-end (E2E) planning in autonomous driving are still largely sensor-centric, leaving rich map priors—HD/SD vector maps, rasterized SD maps, and satellite imagery—underused due to heterogeneity, pose drift, and inconsistent availability at test time. We present emph{UMPE}, a U…
With the emergence of new topics on social media as sources of rumor dissemination, addressing the distribution shifts between source and target domains remains a crucial task in cross-domain rumor detection. Existing feature alignment methods, which aim to reduce the discrepancies between domains,…
Recent advancements in high-definition (HD) map construction have demonstrated the effectiveness of dense representations, which heavily rely on computationally intensive bird’s-eye view (BEV) features. While sparse representations offer a more efficient alternative by avoiding dense BEV processing,
Trajectory prediction in autonomous driving relies on accurate representation of all relevant contexts of the driving scene, including traffic participants, road topology, traffic signs, as well as their semantic relations to each other. Despite increased attention to this issue, most approaches in