feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

platform/as_platform/data/ingest/dms_coco.py

@@ -0,0 +1,88 @@
+"""DMS COCO-format adapter."""
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+from as_platform.data.ingest.base import IngestAdapter, IngestContext, NormalizedDataset
+
+COCO_NAMES = ("instances_train.json", "instances_val.json", "instances_test.json", "annotations.json")
+
+
+def _read_json(path: Path) -> dict[str, Any] | None:
+    try:
+        return json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        return None
+
+
+class DmsCocoAdapter(IngestAdapter):
+    format_id = "dms_coco"
+    projects = ("dms",)
+
+    def _find_coco_files(self, root: Path) -> list[Path]:
+        files: list[Path] = []
+        for name in COCO_NAMES:
+            p = root / "annotations" / name
+            if p.is_file():
+                files.append(p)
+        for name in COCO_NAMES:
+            p = root / name
+            if p.is_file():
+                files.append(p)
+        return files
+
+    def can_handle(self, ctx: IngestContext) -> bool:
+        root = ctx.source_path
+        return len(self._find_coco_files(root)) > 0
+
+    def inspect(self, ctx: IngestContext) -> NormalizedDataset:
+        root = ctx.source_path
+        files = self._find_coco_files(root)
+        split_counts = {"train": 0, "val": 0, "test": 0}
+        ann_count = 0
+        categories: set[str] = set()
+        warnings: list[str] = []
+        for f in files:
+            data = _read_json(f)
+            if not data:
+                warnings.append(f"failed to parse {f.name}")
+                continue
+            images = data.get("images") or []
+            anns = data.get("annotations") or []
+            cats = data.get("categories") or []
+            ann_count += len(anns)
+            for c in cats:
+                name = c.get("name")
+                if isinstance(name, str):
+                    categories.add(name)
+            lower = f.name.lower()
+            if "train" in lower:
+                split_counts["train"] += len(images)
+            elif "val" in lower:
+                split_counts["val"] += len(images)
+            elif "test" in lower:
+                split_counts["test"] += len(images)
+            else:
+                split_counts["train"] += len(images)
+
+        return NormalizedDataset(
+            format_id=self.format_id,
+            project=ctx.project,
+            task=ctx.task,
+            source_path=str(root),
+            split_counts=split_counts,
+            sample_count=sum(split_counts.values()),
+            annotation_count=ann_count,
+            artifacts=[self._artifact_name(root, f) for f in files],
+            warnings=warnings,
+            extra={"categories": sorted(categories)},
+        )
+
+    @staticmethod
+    def _artifact_name(root: Path, path: Path) -> str:
+        try:
+            return str(path.relative_to(root))
+        except ValueError:
+            return path.name