RAIL-BENCH is the first standardized benchmark suite for railway perception with five challenges, real-world datasets, and a novel LineAP metric for rail track detection.
Hdmapnet: An online HD map construction and evaluation framework
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Argoverse 2 introduces three new datasets with annotated sensor data, massive lidar collections, and challenging motion forecasting scenarios for autonomous driving research.
GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.
MapATM improves lane divider AP by 4.6 and mAP by 2.6 on NuScenes by treating actor trajectories as structural priors for road geometry.
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
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Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain
RAIL-BENCH is the first standardized benchmark suite for railway perception with five challenges, real-world datasets, and a novel LineAP metric for rail track detection.
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Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
Argoverse 2 introduces three new datasets with annotated sensor data, massive lidar collections, and challenging motion forecasting scenarios for autonomous driving research.
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GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes
GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.
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MapATM: Enhancing HD Map Construction through Actor Trajectory Modeling
MapATM improves lane divider AP by 4.6 and mAP by 2.6 on NuScenes by treating actor trajectories as structural priors for road geometry.
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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.