feat: default artifact paths to ~/autoware_data/ml_models (#12523)

feat(launches,configs): default artifact paths to ~/autoware_data/ml_models

Roll every per-package `data_path` / `model_path` launch-arg default
from `$(env HOME)/autoware_data[/...]` to
`$(env HOME)/autoware_data/ml_models[/...]` so standalone universe
launches resolve artifacts under the new `~/autoware_data/ml_models/`
layout (autowarefoundation/autoware#7068).

When invoked through autoware_launch the parent overrides cascade and
already pin the new root (autowarefoundation/autoware_launch#1835); this
commit closes the gap for users who launch a perception / localization /
sensing / planning component directly with `ros2 launch <pkg>`.

22 launch files updated (one-line default change each):

- e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml
- localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml
- perception/autoware_bevfusion/launch/bevfusion.launch.xml
- perception/autoware_camera_streampetr/launch/streampetr.launch.xml
- perception/autoware_image_projection_based_fusion/launch/pointpainting_fusion.launch.xml
- perception/autoware_lidar_apollo_instance_segmentation/launch/lidar_apollo_instance_segmentation.launch.xml
- perception/autoware_lidar_centerpoint/launch/lidar_centerpoint.launch.xml
- perception/autoware_lidar_frnet/launch/lidar_frnet.launch.xml
- perception/autoware_lidar_transfusion/launch/lidar_transfusion.launch.xml
- perception/autoware_ptv3/launch/ptv3.launch.xml
- perception/autoware_shape_estimation/launch/shape_estimation.launch.xml
- perception/autoware_simpl_prediction/launch/simpl.launch.xml
- perception/autoware_tensorrt_bevdet/launch/tensorrt_bevdet.launch.xml
- perception/autoware_tensorrt_bevformer/launch/bevformer.launch.xml
- perception/autoware_tensorrt_yolox/launch/{yolox_traffic_light_detector,yolox_tiny,yolox_s_plus_opt}.launch.xml
- perception/autoware_traffic_light_classifier/launch/{car,pedestrian}_traffic_light_classifier.launch.xml
- perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml
- planning/autoware_diffusion_planner/launch/diffusion_planner.launch.xml
- sensing/autoware_calibration_status_classifier/launch/calibration_status_classifier.launch.xml

Drive-by README and test fixes:

- e2e/autoware_tensorrt_vad/{README.md,docs/design.md}: also migrate the
  `$HOME/autoware_map/Town01` examples to `$HOME/autoware_data/maps/Town01`.
- localization/yabloc/{README.md,yabloc_pose_initializer/README.md}: also
  migrate `$HOME/autoware_map/sample-map-rosbag` to
  `$HOME/autoware_data/maps/demos/sample-map-rosbag`.
- control/autoware_smart_mpc_trajectory_follower/README.md: migrate the
  `map_path:=$HOME/autoware_map/sample-map-planning` example to
  `$HOME/autoware_data/maps/demos/sample-map-planning`.
- simulator/autoware_carla_interface/README.md: migrate every
  `$HOME/autoware_map/Town01/...` reference to
  `$HOME/autoware_data/maps/Town01/...`.
- perception/{autoware_bevfusion,autoware_image_projection_based_fusion,autoware_lidar_centerpoint,autoware_tensorrt_bevformer}/README.md: copy-paste examples updated to `~/autoware_data/ml_models/<pkg>`.
- perception/autoware_camera_streampetr/config/ml_package_camera_streampetr.param.yaml: header comment updated.
- planning/autoware_diffusion_planner/README.md: prerequisites snippet updated.
- sensing/autoware_calibration_status_classifier/test/{test_model_inference,test_calibration_status_classifier}.cpp: hardcoded fallback ONNX path updated.

Users on the legacy layout can pin the old root with
`data_path:=$HOME/autoware_data` (or the per-package equivalent) on the
command line.

Refs: autowarefoundation/autoware#7068

Signed-off-by: Mete Fatih Cırıt <mfc@autoware.org>

Author: xmfcx

control/autoware_smart_mpc_trajectory_follower/README.md

@@ -40,7 +40,7 @@ In "mppi_ilqr" mode, the initial value of iLQR is given by the MPPI solution.
 To perform a simulation, run the following command:
 
 ```bash
-ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower
+ros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_data/maps/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit trajectory_follower_mode:=smart_mpc_trajectory_follower
 ```
 
 > [!NOTE]

e2e/autoware_tensorrt_vad/README.md

@@ -36,7 +36,7 @@ This module replaces traditional localization, perception, and planning modules
 Parameters can be set via configuration files:
 
 - Deployment configuration (node and interface parameters): `config/vad_carla_tiny.param.yaml`
-- Model architecture parameters: `vad-carla-tiny.param.json` (downloaded with model to `~/autoware_data/vad/v0.1/`)
+- Model architecture parameters: `vad-carla-tiny.param.json` (downloaded with model to `~/autoware_data/ml_models/vad/v0.1/`)
 
 ---
 
@@ -99,7 +99,7 @@ Then launch the E2E VAD system:
 
 ```bash
 ros2 launch autoware_launch e2e_simulator.launch.xml \
-  map_path:=$HOME/autoware_map/Town01 \
+  map_path:=$HOME/autoware_data/maps/Town01 \
   vehicle_model:=sample_vehicle \
   sensor_model:=carla_sensor_kit \
   simulator_type:=carla \
@@ -117,7 +117,7 @@ The VAD model files are automatically downloaded when setting up the Autoware de
 To download the latest models, simply run the provided setup script:
 [How to set up a development environment](https://autowarefoundation.github.io/autoware-documentation/main/installation/autoware/source-installation/#how-to-set-up-a-development-environment)
 
-The models will be downloaded to `~/autoware_data/vad/` by default.
+The models will be downloaded to `~/autoware_data/ml_models/vad/` by default.
 
 **Manual Download** (if needed):
 Models are hosted at: <https://awf.ml.dev.web.auto/planning/models/tensorrt_vad/carla_tiny/v0.1/>

e2e/autoware_tensorrt_vad/docs/design.md

@@ -197,7 +197,7 @@ flowchart TD
 
 ### Separation between model architecture and deployment parameters
 
-- Model architecture parameters (network structure, normalization, training dataset classes) are defined in `vad-carla-tiny.param.json` (downloaded with model to `~/autoware_data/vad/v0.1/`)
+- Model architecture parameters (network structure, normalization, training dataset classes) are defined in `vad-carla-tiny.param.json` (downloaded with model to `~/autoware_data/ml_models/vad/v0.1/`)
 - Deployment parameters (hardware settings, file paths, detection thresholds) are configured in [`vad_carla_tiny.param.yaml`](../config/vad_carla_tiny.param.yaml)
   - Object class remapping parameters are added to [`object_class_remapper_carla_tiny.param.yaml`](../config/object_class_remapper_carla_tiny.param.yaml)
     - Following the precedent of [`autoware_bevfusion`](../../../perception/autoware_bevfusion/README.md)

e2e/autoware_tensorrt_vad/launch/vad_carla_tiny.launch.xml

@@ -22,7 +22,7 @@
   <!-- ========================================
        Model and Plugin Paths
        ======================================== -->
-  <arg name="data_path" default="$(env HOME)/autoware_data" description="Root directory for data and models"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data/ml_models" description="Root directory for data and models"/>
   <arg name="model_path" default="$(var data_path)/vad" description="VAD ONNX models and TensorRT engines directory"/>
   <arg name="plugins_path" default="$(find-pkg-share autoware_tensorrt_plugins)" description="Custom TensorRT plugins library"/>
 

localization/yabloc/README.md

@@ -24,7 +24,7 @@ A sample command to run YabLoc is as follows
 
 ```shell
 ros2 launch autoware_launch logging_simulator.launch.xml \
-  map_path:=$HOME/autoware_map/sample-map-rosbag\
+  map_path:=$HOME/autoware_data/maps/sample-map-rosbag\
   vehicle_model:=sample_vehicle \
   sensor_model:=sample_sensor_kit \
   pose_source:=yabloc

localization/yabloc/yabloc_pose_initializer/README.md

@@ -10,10 +10,10 @@ It is also possible to download it manually. Even if the model is not downloaded
 To download and extract the model manually:
 
 ```bash
-$ mkdir -p ~/autoware_data/yabloc_pose_initializer/
-$ wget -P ~/autoware_data/yabloc_pose_initializer/ \
+$ mkdir -p ~/autoware_data/ml_models/yabloc_pose_initializer/
+$ wget -P ~/autoware_data/ml_models/yabloc_pose_initializer/ \
        https://autoware-files.s3.us-west-2.amazonaws.com/models/yabloc/136_road-segmentation-adas-0001/resources.tar.gz
-$ tar xzf ~/autoware_data/yabloc_pose_initializer/resources.tar.gz -C ~/autoware_data/yabloc_pose_initializer/
+$ tar xzf ~/autoware_data/ml_models/yabloc_pose_initializer/resources.tar.gz -C ~/autoware_data/ml_models/yabloc_pose_initializer/
 ```
 
 ## Note

localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml

@@ -1,5 +1,5 @@
 <launch>
-  <arg name="data_path" default="$(env HOME)/autoware_data"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data/ml_models"/>
   <arg name="model_path" default="$(var data_path)/yabloc_pose_initializer/saved_model/model_float32.pb"/>
 
   <node pkg="yabloc_pose_initializer" exec="yabloc_pose_initializer_node" output="both">

perception/autoware_bevfusion/README.md

@@ -69,7 +69,7 @@ This node assumes that the input pointcloud follows the `PointXYZIRC` layout def
 ## Trained Models
 
 You can download the onnx and config files in the following links.
-The files need to be placed inside `$(env HOME)/autoware_data/bevfusion`
+The files need to be placed inside `$(env HOME)/autoware_data/ml_models/bevfusion`
 
 - lidar-only model:
   - [onnx](https://awf.ml.dev.web.auto/perception/models/bevfusion/t4base_120m/v1/bevfusion_lidar.onnx)

perception/autoware_bevfusion/launch/bevfusion.launch.xml

@@ -2,7 +2,7 @@
 <launch>
   <arg name="input/pointcloud" default="/sensing/lidar/concatenated/pointcloud"/>
   <arg name="output/objects" default="objects"/>
-  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data/ml_models" description="packages data and artifacts directory path"/>
   <arg name="model_name" default="bevfusion_lidar">
     <choice value="bevfusion_lidar"/>
     <choice value="bevfusion_camera_lidar"/>

perception/autoware_camera_streampetr/config/ml_package_camera_streampetr.param.yaml

@@ -1,5 +1,5 @@
 # This is just an example file, the actual model param file comes with the model onnx files
-# and is stored in the artifacts directory (~/autoware_data/camera_streampetr by default).
+# and is stored in the artifacts directory (~/autoware_data/ml_models/camera_streampetr by default).
 
 /**:
   ros__parameters: