docs: comlops dataset design

README.md

@@ -1,12 +1,37 @@
-# tier4_perception_dataset
+# TIER IV Perception Dataset Conversion Tools
 
-This is the data conversion tools around T4 dataset.
+This repository provides tools for converting ROS2 bag files and TIER IV (T4) dataset files, as well as the design specifications for the T4 dataset format.  
+These tools facilitate the preparation and transformation of perception data for autonomous driving applications.
 
-## Tools Overview
+## Table of Contents
+
+1. [Dataset Overview](#dataset-overview)
+2. [Usage of T4 dataset](#usage-of-t4-format-dataset)
+3. [Usage of Conversion Tools](#usage-of-conversion-tools)
+   - [Conversion Tools Overview](#conversion-tools-overview)
+   - [Setup](#setup)
+   - [Test](#test)
+   - [Pre Commit](#pre-commit)
+
+## Dataset Overview
+
+The T4 dataset is designed to support autonomous driving research and development.  
+It can include various types of perception data such as images and lidar point clouds, radar point clouds, along with annotations for tasks like object detection, segmentation, and tracking.
+
+For detailed information about the T4 dataset format, please refer to the [T4 Dataset Format Documentation](docs/t4_format_3d_detailed.md).
+
+## Usage of T4 format dataset
+
+t4-devkit is a development kit for working with the T4 dataset, providing additional utilities and tools.  
+Please see [t4-devkit](t4-devkit/README.md) about details.
+
+## Usage of Conversion Tools
+
+### Conversion Tools Overview
 
 See [tools_overview](docs/tools_overview.md) about the converters.
 
-## Setup
+### Setup
 
 ```bash
 git clone git@github.com:tier4/tier4_perception_dataset.git perception_dataset
@@ -34,9 +59,9 @@ pip3 install poetry
 poetry install
 ```
 
-## Test
+### Test
 
-### Download test data
+#### Download test data
 
 - [GitHub CLI](https://github.com/cli/cli#installation)
 
@@ -47,15 +72,15 @@ unzip 'tests/data/*.zip' -d tests/data/
 
 or manually download zipped data from [the release page](https://github.com/tier4/tier4_perception_dataset/releases/tag/test-data) to a `test/data` directory
 
-### Run tests
+#### Run tests
 
 ```bash
 source /opt/ros/${ROS_DISTRO}/setup.sh
 source ./install/setup.bash
 poetry run pytest
 ```
 
-## Pre commit
+### Pre commit
 
 ```bash
 # to install hooks of formatter and linter files

docs/t4_format_3d_detailed.md

@@ -9,11 +9,13 @@
 - (2021.11.30) T4 format ver1.1: Updated format to make it available in ADP
 - (2022.06.03) T4 format ver1.2: Update topics contained in input_bag. Add status.json. Remove occlusion_state in attribute, write to visibility.json
 - (2022.07.26) T4 format ver1.2: Add a topic contained in input_bag.
+- (2024.07.24) T4 Format ver1.3: Added optional values for the T4 format, which are used in the Co-MLOps dataset format.
 
 ## Dataset Sample
 
-You can download one sample dataset manually from [here](https://drive.google.com/file/d/1UjMWZj5Yc55O7BZiGHa0ikZGhwmcfPiS/view).  
-For this sample, the directory named "sample_dataset" corresponds to the "T4 dataset ID" described in the following section.
+You can download one sample T4 format dataset manually from [here](https://drive.google.com/file/d/1UjMWZj5Yc55O7BZiGHa0ikZGhwmcfPiS/view).  
+For this sample, the directory named "sample_dataset" corresponds to the "T4 dataset ID" described in the following section.  
+Note: This sample dataset contains only lidar point cloud sensor data and includes annotations.
 
 ## Directory Structure
 
@@ -36,6 +38,7 @@ For this sample, the directory named "sample_dataset" corresponds to the "T4 dat
     - scene.json
     - sensor.json
     - visibility.json
+    - vehicle_state.json (Optional; Necessary for Co-MLOps dataset)
     - object_ann.json (If 2D annotation exists)
     - surface_ann.json (If 2D annotation exists)
   - data/
@@ -303,8 +306,6 @@ its calibration is not done. So, if cameras used in collecting the data have any
   - "token": [str] -- Unique record identifier.
   - "sensor_token": [str] -- Foreign key pointing to the sensor type.
   - "translation": [float] [3] -- Coordinate system origin in meters: (x, y, z).
-  - "velocity": [Optional[float]] [3] -- Coordinates system origin in m/s: (vx, vy, vz). **(Added)**
-  - "acceleration": [Optional[float]] [3] -- Coordinates system origin in m/s^2: (ax, ay, az). **(Added)**
   - "rotation": [float] [4] -- Coordinate system orientation as - quaternion: (w, x, y, z).
   - "camera_intrinsic": [float] [3, 3] -- Intrinsic camera calibration. Empty list `[]` for sensors other than cameras.
   - "camera_distortion": [float] [5] -- Distortion parameters (k1, k2, p1, p2, k3) Empty list '[]' for sensors other than cameras. **(Added)**
@@ -359,18 +360,9 @@ For t4 format, "name" should be one of the following:
   - "translation": [float] [3] -- Coordinate system origin in meters: x, y, z. ~~Note that z is always 0~~. **(changed)**
   - "rotation": [float] [4] -- Coordinate system orientation as - quaternion: w, x, y, z.
   - "timestamp": [int] -- Unix time stamp (μ sec).
-
-#### Future extension
-
-- We plan to extend the twist data
-  - make when rosbag to Tier4 dataset
-- ego_pose
-  - "token": [str] -- Unique record identifier.
-  - "translation": [float] [3] -- Coordinate system origin in meters: x, y, z. ~~Note that z is always 0.~~
-  - "rotation": [float] [4] -- Coordinate system orientation as - quaternion: w, x, y, z.
-  - "twist": [float] [6] -- Coordinate system origin in m/s and deg/s : vel_x, vel_y, vel_z, yaw_rate, pitch_rate, roll_rate.
-  - "accel": [float] [2] -- Coordinate system origin in m/s^2 : long_accel, lat_accel.
-  - "timestamp": [int] -- Unix time stamp (μ sec).
+  - "twist": [Optional[float]] [6] -- Coordinates system origin in m/s: (vel_x, vel_y, vel_z, yaw_rate, pitch_rate, roll_rate). **(Added)**
+  - "acceleration": [Optional[float]] [3] -- Coordinates system origin in m/s^2: (ax, ay, az). **(Added)**
+  - "geocoordinate": [Optional[float]] [3] -- Coordinates system origin in the WGS 84 reference ellipsoid: (latitude, longitude, altitude). **(Added)**
 
 ### instance.json
 
@@ -579,6 +571,33 @@ For T4 annotated dataset, visibility is classified into 4 bins below:
   - "level": [str] -- Visibility level.
   - "description": [str] -- Description of visibility level.
 
+### vehicle_state.json
+
+A description of vehicle status.
+
+#### Description
+
+The vehicle_state.json file is an optional component of the T4 dataset format, necessary for the Co-MLOps dataset.  
+This file provides comprehensive information about the vehicle's state at a given timestamp, including the status of doors, indicators, steering data, and other relevant information.
+
+#### Items
+
+- vehicle_state
+  - "token": [str] -- Unique record identifier.
+  - "timestamp": [int] -- Unix time stamp (μ sec).
+  - "accel_pedal": [Optional[float]] -- Accel pedal position [%]
+  - "brake_pedal": [Optional[float]] -- Brake pedal position [%]
+  - "steer_pedal": [Optional[float]] -- Steering wheel position [%]
+  - "steering_tire_angle": [Optional[float]] -- Steering tire angle [rad]
+  - "steering_wheel_angle": [Optional[float]] -- steering wheel angle [rad]
+  - "shift_state": [Optional[str]] {PARK, REVERSE, NEUTRAL, HIGH, FORWARD, LOW, NONE} -- Shift state.
+  - "indicators": [Optional[str]] [3]
+    - "left": [Optional[str]] -- State of the left indicator ("on" or "off").
+    - "right": [Optional[str]] -- State of the right indicator ("on" or "off").
+    - "hazard": [Optional[str]] -- State of the hazard lights ("on" or "off").
+  - "additional_info":
+    - "speed": [Optional[float]] -- Speed of the vehicle in km/h.
+
 ### Reference
 
 - nuScenes: <https://www.nuscenes.org/nuscenes#data-format>