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A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges
This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges.
Collaborative Perception Datasets in Autonomous Driving: A Survey
This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X).
Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization
Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the system's prediction.
Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets
RALF provides plausibility labels for radar raw detections, distinguishing between artifacts and targets.
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