feat(T4Dataset): Add lidar and lidarseg metadata to t4dataset cache generator

autoware_ml/common/enums/enums.py

@@ -15,6 +15,21 @@
 from enum import Enum
 
 
+class Modality(str, Enum):
+    """
+    Modality.
+
+    Attributes:
+      LIDAR: Lidar modality.
+      CAMERA: Camera modality.
+      RADAR: Radar modality.
+    """
+
+    LIDAR = "lidar"
+    CAMERA = "camera"
+    RADAR = "radar"
+
+
 class SplitType(str, Enum):
     """
     Split type.

autoware_ml/configs/database/t4dataset/t4dataset_base.yaml

@@ -18,5 +18,8 @@ defaults:
   - t4dataset/scenarios@scenarios.db_jpntaxi_base: detection3d/db_jpntaxi_base
   - t4dataset/scenarios@scenarios.db_largebus: detection3d/db_largebus
 
+# Number of features in the lidar pointcloud
+lidar_pointcloud_num_features: 5
+
 # Processor settings
 num_workers: 16

autoware_ml/configs/database/t4dataset/t4dataset_j6gen2_base.yaml

@@ -16,5 +16,8 @@ defaults:
   - t4dataset/scenarios@scenarios.db_j6gen2: detection3d/db_j6gen2
   - t4dataset/scenarios@scenarios.db_largebus: detection3d/db_largebus
 
+# Number of features in the lidar pointcloud
+lidar_pointcloud_num_features: 5
+
 # Processor settings
 num_workers: 16

autoware_ml/configs/database/t4dataset/t4dataset_jpntaxi_base.yaml

@@ -14,5 +14,8 @@ cache_file_prefix_name: database
 defaults:
   - t4dataset/scenarios@scenarios.db_jpntaxi_base: detection3d/db_jpntaxi_base
 
+# Number of features in the lidar pointcloud
+lidar_pointcloud_num_features: 5
+
 # Processor settings
 num_workers: 16

autoware_ml/databases/base_database.py

@@ -22,7 +22,7 @@
 import polars as pl
 
 from autoware_ml.databases.scenarios import Scenarios, ScenarioData
-from autoware_ml.databases.schemas import DatasetRecord, DatasetTableSchema
+from autoware_ml.databases.schemas.dataset_schemas import DatasetRecord, DatasetTableSchema
 
 logger = logging.getLogger(__name__)
 

autoware_ml/databases/database_interface.py

@@ -19,7 +19,7 @@
 from types import MappingProxyType
 
 from autoware_ml.databases.scenarios import Scenarios, ScenarioData
-from autoware_ml.databases.schemas import DatasetRecord
+from autoware_ml.databases.schemas.dataset_schemas import DatasetRecord
 
 
 class DatabaseInterface(Protocol):

autoware_ml/databases/schemas/base_schemas.py

@@ -0,0 +1,87 @@
+# Copyright 2026 TIER IV, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import NamedTuple, Sequence, Mapping, Any
+
+import polars as pl
+
+
+class DatasetTableColumn(NamedTuple):
+    """
+    Annotation table column.
+
+    Attributes:
+      name: Name of the column.
+      dtype: Data type of the column.
+    """
+
+    name: str
+    dtype: pl.DataType
+
+
+class BaseFieldSchema:
+    """
+    Base class for field schemas.
+    """
+
+    @classmethod
+    def to_polars_field_schema(cls) -> Sequence[pl.Field]:
+        """
+        Convert the lidar column schema to a Polars field schema.
+
+        Returns:
+          pl.Schema: Polars schema.
+        """
+
+        return [
+            pl.Field(v.name, v.dtype)
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__") and isinstance(v, DatasetTableColumn)
+        ]
+
+
+class DataModelInterface(ABC):
+    """
+    Interface for data models.
+    """
+
+    @abstractmethod
+    def to_dictionary(self) -> Mapping[str, Any]:
+        """
+        Convert the data model to a dictionary.
+
+        Returns:
+          Mapping[str, Any]: Dictionary representation of the data model.
+        """
+
+        raise NotImplementedError("Subclasses must implement to_dictionary!")
+
+    @classmethod
+    def load_from_dictionary(cls, data_model: Mapping[str, Any]) -> DataModelInterface:
+        """
+        Load the data model and decode it to the corresponding data model from a dictionary, which is
+        deserialized from a Polars dataframe.
+
+        Args:
+          data_model: Dictionary representation of the data model, which is
+          deserialized from a Polars dataframe.
+
+        Returns:
+          DataModelInterface: Data model.
+        """
+
+        raise NotImplementedError("Subclasses must implement load_from_dictionary!")

autoware_ml/databases/schemas/category_mapping.py

@@ -0,0 +1,85 @@
+# Copyright 2026 TIER IV, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Sequence, Any, Mapping
+
+import polars as pl
+from pydantic import BaseModel, ConfigDict
+
+from autoware_ml.databases.schemas.base_schemas import (
+    BaseFieldSchema,
+    DatasetTableColumn,
+    DataModelInterface,
+)
+
+
+@dataclass(frozen=True)
+class CategoryMappingDatasetSchema(BaseFieldSchema):
+    """
+    Dataclass to define polars schema for columns related to category mapping.
+    """
+
+    CATEGORY_NAMES = DatasetTableColumn("category_names", pl.List(pl.String))
+    CATEGORY_INDICES = DatasetTableColumn("category_indices", pl.List(pl.Int32))
+
+
+class CategoryMappingDataModel(BaseModel, DataModelInterface):
+    """
+    Category mapping data model that can be shared by multiple datasets. It saves the mapping
+      between category names and category indices.
+
+    Attributes:
+      category_names: List of category names.
+      category_indices: List of category indices.
+    """
+
+    model_config = ConfigDict(frozen=True, strict=True, arbitrary_types_allowed=True)
+
+    category_names: Sequence[str]
+    category_indices: Sequence[int]
+
+    def model_post_init(self, __context: Any) -> None:
+        """Validate that all attributes are of the same length."""
+
+        assert len(self.category_names) == len(self.category_indices), (
+            "All attributes must be of the same length"
+        )
+
+    def to_dictionary(self) -> Mapping[str, Any]:
+        """
+        Convert the category mapping data model to a dictionary.
+
+        Returns:
+          Mapping[str, Any]: Dictionary representation of the category mapping data model.
+        """
+
+        return self.model_dump()
+
+    @classmethod
+    def load_from_dictionary(cls, data_model: Mapping[str, Any]) -> CategoryMappingDataModel:
+        """
+        Load the category mapping data model and decode it to the corresponding CategoryMappingDataModel
+        from a dictionary, which is deserialized from a Polars dataframe.
+
+        Args:
+          data_model: Dictionary representation of the category mapping data model, which is
+          deserialized from a Polars dataframe.
+        """
+        return cls(
+            category_names=data_model[CategoryMappingDatasetSchema.CATEGORY_NAMES.name],
+            category_indices=data_model[CategoryMappingDatasetSchema.CATEGORY_INDICES.name],
+        )