Change directory and fix broken links and indentation for mkdocs. (#164)

README.md

@@ -4,176 +4,5 @@
 
 AutowareArchitectureProposal is a repository to explore and establish the architecture design of Autoware, an autonomous driving software managed by Autoware Foundation.
 
-There already exists [Autoware.Auto](https://gitlab.com/autowarefoundation/autoware.auto) repository in Autoware Foundation GitLab. The architecture investigation, however, was done as a separate repository rather than a fork to explore architecture without prejudice from the existing source code.  
-
-The established architecture will be presented to Autoware.Auto which will then be reviewed by the community members and be used to improve Autoware.Auto. AutowareArchitectureProposal also contains new functions that do not yet exist in Autoware.Auto to verify that the architecture is feasible for various use cases. These functions are also planned to be refactored and merged into Autoware.Auto. The details are explained in the [Future plans](#future-plans) section.  
-
-## Note for non-Tier IV members
-
-- All source code relating to this meta-repository is intended solely to demonstrate a potential new architecture for Autoware
-- Please do not use any source code related to this repository to control actual vehicles
-- While the documentation is published on GitHub Pages, the repository is private. We don't accept any contributions from outside of Tier IV
-
-## Target
-
-AutowareArchitectureProposal aims to realize autonomous driving in various environments.  
-Autonomous driving on public roads is an extremely challenging project, and it cannot be achieved in a short period of time. Therefore we are trying to accumulate technology by applying the current AutowareArchitectureProposal to more restricted use cases such as in-factory transportation. At the same time, demonstration experiments in public roads are also in progress.  
-
-## Future plans
-
-Again, autonomous driving is an extremely challenging project and this cannot be achieved in a short time. It cannot be achieved by only one company. People and companies all over the world need to work together to make it possible. We build the system and society through collaboration. So [Tier IV established the Autoware Foundation (AWF) in 2018](https://tier4.jp/cn/media/news/the-autoware-foundation/) to initiate, grow, and fund Autoware-related projects worldwide. [Autoware.Auto](https://www.autoware.auto/) is the current flagship AWF project and is a complete architectural redesign of [Autoware.AI](https://github.com/Autoware-AI/autoware.ai) that employs best-in-class software engineering practices.  
-As part of Tier IV's commitment to collaboration with the AWF and its members, we plan to merge the additional functionality of AutowareArchitectureProposal to Autoware.Auto. Note that since Autoware.Auto has its own scope and ODD (Operational Design Domain, prerequisite environmental conditions for an automatic driving system to operate) that needs to be achieved, not all the features in AutowareArchitectureProposal will be required.
-
-We keep using AutowareArchitectureProposal for some time, but remember that the core of our products will shift to Autoware.Auto.
-
-## autoware.proj
-
-autoware.proj is a meta-repository for AutowareArchitectureProposal.  
-Since AutowareArchitectureProposal is made up of code stored in multiple, version-specific GitHub repositories, creating a new build environment would involve individually importing each repository which is both time-consuming and prone to error. To avoid both of these problems, autoware.proj was created as a meta-repository to streamline the management of all necessary repositories through the use of [vcstool](https://github.com/dirk-thomas/vcstool).  
-
-autoware.proj is the top directory of the AutowareArchitectureProposal project. Therefore, this repository contains high-level information of AutowareArchitectureProposal such as [the architecture design](/design/Overview.md).
-
-## Installation
-
-### Minimum Requirements
-
-#### Hardware
-
-- x86 CPU (8 cores)
-- 16GB RAM
-- [Optional] NVIDIA GPU (4GB RAM)
-  - Although not required to run basic functionality, a GPU is mandatory in order to run the following components:
-    - lidar_apollo_instance_segmentation
-    - traffic_light_ssd_fine_detector
-    - cnn_classifier
-
-> Performance will be improved with more cores, RAM and a higher-spec graphics card.
-
-#### Software
-
-- Ubuntu 20.04
-- NVIDIA driver
-
-### Review licenses
-
-The following software will be installed during the installation process, so please confirm their licenses first before proceeding.
-
-- [CUDA 11.1](https://docs.nvidia.com/cuda/eula/index.html)
-- [cuDNN 8](https://docs.nvidia.com/deeplearning/sdk/cudnn-sla/index.html)
-- [OSQP](https://github.com/oxfordcontrol/osqp/blob/master/LICENSE)
-- [ROS 2 Foxy](https://index.ros.org/doc/ros2/Releases/Release-Foxy-Fitzroy/)
-- [TensorRT 7](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html)
-
-### Installation steps
-
-> If the CUDA or TensorRT frameworks have already been installed, we strongly recommend uninstalling them first.
-
-1. Set up the Autoware repository
-
-```sh
-mkdir -p ~/workspace
-cd ~/workspace
-git clone git@github.com:tier4/autoware.proj.git
-cd autoware.proj
-```
-
-2. Run the setup script to install CUDA, cuDNN 8, OSQP, ROS 2 and TensorRT 7, entering 'y' when prompted (this step will take around 45 minutes)
-
-```sh
-./setup_ubuntu20.04.sh
-```
-
-3. Build the source code (this will take around 15 minutes)
-
-```sh
-source ~/.bashrc
-colcon build --cmake-args -DCMAKE_BUILD_TYPE=Release --catkin-skip-building-tests
-```
-
-> Several modules will report stderr output, but these are just warnings and can be safely ignored.
-
-### Sensor hardware configuration
-
-Prepare launch and vehicle description files according to the sensor configuration of your hardware.  
-The following files are provided as samples:
-
-- [sensing.launch](https://github.com/tier4/autoware_launcher.universe/blob/master/sensing_launch/launch/sensing.launch)
-- [lexus_description](https://github.com/tier4/lexus_description.iv.universe)
-
-## Running Autoware
-
-### Quick Start
-
-#### Rosbag simulation
-
-1. [Download the sample pointcloud and vector maps](https://drive.google.com/open?id=1ovrJcFS5CZ2H51D8xVWNtEvj_oiXW-zk), unpack the zip archive and copy the two map files to the same folder.
-2. Download the sample rosbag files and put them into the same folder, e.g., `~/rosbag2/sample/`.
-   - [db3](https://drive.google.com/file/d/1wLWyOlfH_-k4VYBgae1KAFlKdwJnH_si/view?usp=sharing)
-   - [yaml](https://drive.google.com/file/d/1Arb-QVnNHM-BFdB_icm7J7fWkyuZt7mZ/view?usp=sharing)
-3. Open a terminal and launch Autoware
-
-```sh
-cd ~/workspace/autoware.proj
-source install/setup.bash
-ros2 launch autoware_launch logging_simulator.launch.xml map_path:=/path/to/map_folder vehicle_model:=lexus sensor_model:=aip_xx1 rosbag:=true perception:=false
-```
-
-4. Open a second terminal and play the sample rosbag file
-
-```sh
-cd ~/workspace/autoware.proj
-source install/setup.bash
-ros2 bag play /path/to/sample.625-2.bag2_0.db3 -r 0.2
-```
-
-5. Focus the view on the ego vehicle by changing the `Target Frame` in the RViz Views panel from `viewer` to `base_link`.
-
-##### Note
-
-- Sample map and rosbag: © 2020 Tier IV, Inc.
-  - Due to privacy concerns, the rosbag does not contain image data, and so traffic light recognition functionality cannot be tested with this sample rosbag. As a further consequence, object detection accuracy is decreased.
-
-#### Planning Simulator
-
-1. [Download the sample pointcloud and vector maps](https://drive.google.com/open?id=197kgRfSomZzaSbRrjWTx614le2qN-oxx), unpack the zip archive and copy the two map files to the same folder.
-2. Open a terminal and launch Autoware
-
-```sh
-cd ~/workspace/autoware.proj
-source install/setup.bash
-ros2 launch autoware_launch planning_simulator.launch.xml map_path:=/path/to/map_folder vehicle_model:=lexus sensor_model:=aip_xx1
-```
-
-3. Set an initial pose for the ego vehicle
-   - a) Click the `2D Pose estimate` button in the toolbar, or hit the `P` key
-   - b) In the 3D View pane, click and hold the left-mouse button, and then drag to set the direction for the initial pose.
-4. Set a goal pose for the ego vehicle
-   - a) Click the `2D Nav Goal` button in the toolbar, or hit the `G` key
-   - b) In the 3D View pane, click and hold the left-mouse button, and then drag to set the direction for the goal pose.
-5. Engage the ego vehicle.
-   - a) Open the [autoware_web_controller](http://localhost:8085/autoware_web_controller/) in a browser.
-   - b) Click the `Engage` button.
-
-##### Note
-
-- Sample map: © 2020 Tier IV, Inc.
-
-### Detailed tutorial instructions
-
-Please refer to the [Simulation tutorial](./docs/SimulationTutorial.md) for more details about supported simulations, along with more verbose instructions including screenshots.
-
-## References
-
-### Videos
-
-- [Scenario demo](https://youtu.be/kn2bIU_g0oY)
-- [Obstacle avoidance in the same lane](https://youtu.be/s_4fBDixFJc)
-- [Obstacle avoidance by lane change](https://youtu.be/SCIceXW9sqM)
-- [Object recognition](https://youtu.be/uhhMIxe1zxQ)
-- [Auto parking](https://youtu.be/e9R0F0ZJbWE)
-- [360° FOV perception (Camera Lidar Fusion)](https://youtu.be/whzx-2RkVBA)
-- [Robustness of localization](https://youtu.be/ydPxWB2jVnM)
-
-### Credits
-
-- [Neural Network Weight Files](./docs/Credits.md)
+# Documentation
+See [Github Pages](https://tier4.github.io/autoware.proj/pulls/164/docs/Readme/)