ARETE applies attention-based rasterized encoding with HSV transformation to crowdsourced trajectories for estimating vectorized lane topologies including centerlines and dividers.
In: International Conference on Learning Representations (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
GameAD models autonomous driving as a risk-prioritized game among agents via Risk-Aware Topology Anchoring, Minimax Risk-Aware Sparse Attention and related components, yielding safer trajectories than prior end-to-end methods on nuScenes and Bench2Drive.
citing papers explorer
-
ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data
ARETE applies attention-based rasterized encoding with HSV transformation to crowdsourced trajectories for estimating vectorized lane topologies including centerlines and dividers.
-
Not All Agents Matter: From Global Attention Dilution to Risk-Prioritized Game Planning
GameAD models autonomous driving as a risk-prioritized game among agents via Risk-Aware Topology Anchoring, Minimax Risk-Aware Sparse Attention and related components, yielding safer trajectories than prior end-to-end methods on nuScenes and Bench2Drive.