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Policy learning module for LKAlert — Stage 1: Supervised 3-class warm-start.
Action space:
SILENT = 0 normal driving / safe scene / non-ego with no path conflict
OBSERVE = 1 heightened attention: early ego threat, non-ego near ego path,
transitional states
ALERT = 2 imminent ego-relevant collision within reaction window
Stage 1 flow:
1. make_policy_labels.py — per-window action labels from SFT manifests
2. warm_start_trainer.py — supervised CE warm-start of PolicyHead only
3. evaluate_policy.py — full evaluation with per-category action breakdown
"""
from .policy_model import PolicyModel
from .policy_dataset import PolicyDataset, policy_collate_fn
__all__ = ["PolicyModel", "PolicyDataset", "policy_collate_fn"]
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