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This work introduces Clinical Intent Extraction (CIE), the task of recovering these actions as complete structured rec-ords, and the Clinical Intent Representation (CIR), which decomposes each action into its verb, type, coded target, timing, and condition.
The results indicate that safety-oriented training and structured integration of interaction and contextual information can reduce large trajectory-prediction errors, although broader validation across pedestrians, sites, and datasets is required.
The detector, LatAD, jointly learns a latent and a Gaussian-mixture clustering of these regimes (VaDE) and scores anomalies by density rather than reconstruction, factorizing that density over correlation-community subsystems and combining per-community surprises by a cohesion-weighted, sparsity-adaptive statistic.
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