diff --git a/Docs/extra.css b/Docs/extra.css
index 3e452dd8e..470130071 100644
--- a/Docs/extra.css
+++ b/Docs/extra.css
@@ -11,3 +11,28 @@
.vector-zero {
text-align: center;
}
+
+
+table.defTable {
+ border: 1px solid #242424;
+ background-color: #f3f6f6;
+ text-align: left;
+ border-collapse: collapse;
+}
+
+table.defTable thead {
+ background: #ffffff;
+ border-bottom: 1px solid #444444;
+}
+
+table.defTable tr:nth-child(even) {
+ background: #ffffff;
+}
+
+table.defTable thead th {
+ padding: 7px 13px;
+}
+
+table.defTable tbody td{
+ padding: 7px 13px;
+}
diff --git a/Docs/index.md b/Docs/index.md
index 92ef85971..7dcc308af 100644
--- a/Docs/index.md
+++ b/Docs/index.md
@@ -2,224 +2,144 @@
Welcome to the CARLA documentation.
-This page contains the index with a brief explanation on the different sections for clarity.
-Feel free to explore the documentation on your own, however, here are a few tips for newcomers:
+This home page contains an index with a brief description of the different sections in the documentation. Feel free to read in whatever order preferred. In any case, here are a few suggestions for newcomers.
-* __Install CARLA__: visit the [Quickstart installation](start_quickstart.md) to get the CARLA releases or make the build for a desired platform.
-* __Start using CARLA__: there is a section titled [First steps](core_concepts.md), highly recommended for newcomers.
-* __Doubts on the API__: there is a handy [Python API reference](python_api.md) to consult classes and methods.
+* __Install CARLA.__ Either follow the [Quick start installation](start_quickstart.md) to get a CARLA release or [make the build](build_linux.md) for a desired platform.
+* __Start using CARLA.__ The section titled [First steps](core_concepts.md) is an introduction to the most important concepts.
+* __Check the API.__ there is a handy [Python API reference](python_api.md) to look up the classes and methods available.
-Besides that, there is also the CARLA forum where the community gathers to share issues, suggestions and solutions:
+The CARLA forum is available to post any doubts or suggestions that may arise during the reading.
!!! Important
- This is documentation for CARLA 0.9.0 or later. Previous documentation is in the [stable branch](https://carla.readthedocs.io/en/stable/).
+ This documentation refers to CARLA 0.9.0 or later. To read about previous versions, check the [stable branch](https://carla.readthedocs.io/en/stable/).
---
## Getting started
-
- Introduction
-
- — Capabilities and intentions behind the project.
-
- Quickstart installation
-
+ [__Introduction__](start_introduction.md)
+ — What to expect from CARLA.
+ [__Quick start__](start_quickstart.md)
— Get the CARLA releases.
## Building CARLA
-
- Linux build
-
+ [__Linux build__](build_linux.md)
— Make the build on Linux.
-
- Windows build
-
+ [__Windows build__](build_windows.md)
— Make the build on Windows.
-
- Update CARLA
-
+ [__Update CARLA__](build_update.md)
— Get up to date with the latest content.
-
- Build system
-
+ [__Build system__](build_system.md)
— Learn about the build and how it is made.
-
- Running in a Docker
-
+ [__Running in a Docker__](build_docker.md)
— Run CARLA using a container solution.
-
- F.A.Q.
-
- — Some of the most frequent issues for newcomers.
+ [__F.A.Q.__](build_faq.md)
+ — Some of the most frequent installation issues.
## First steps
-
- Core concepts
-
+ [__Core concepts__](core_concepts.md)
— Overview of the basic concepts in CARLA.
-
- 1st. World and client
-
+ [__1st. World and client__](core_world.md)
— Manage and access the simulation.
-
- 2nd. Actors and blueprints
-
+ [__2nd. Actors and blueprints__](core_actors.md)
— Learn about actors and how to handle them.
-
- 3rd. Maps and navigation
-
- — Discover the different maps and how to move around.
-
- 4th. Sensors and data
-
+ [__3rd. Maps and navigation__](core_map.md)
+ — Discover the different maps and how do vehicles move around.
+ [__4th. Sensors and data__](core_sensors.md)
— Retrieve simulation data using sensors.
+
## Advanced steps
-
- Recorder
-
- — Store all the events in a simulation a play it again.
-
- Rendering options
-
- — Different settings, from quality to no-render or off-screen runs.
-
- Synchrony and time-step
-
+ [__Recorder__](adv_recorder.md)
+ — Register the events in a simulation and play it again.
+ [__Rendering options__](adv_rendering_options.md)
+ — From quality settings to no-render or off-screen modes.
+ [__Synchrony and time-step__](adv_synchrony_timestep.md)
— Client-server communication and simulation time.
-
- Traffic Manager
-
- — Feature to handle autopilot vehicles and emulate traffic.
+ [__Traffic Manager__](adv_traffic_manager.md)
+ — Simulate urban traffic by setting vehicles to autopilot mode.
+
## References
-
- Python API reference
-
+ [__Python API reference__](python_api.md)
— Classes and methods in the Python API.
-
- Code recipes
-
- — Code fragments commonly used.
-
- Blueprint library
-
+ [__Code recipes__](ref_code_recipes.md)
+ — Some code fragments commonly used.
+ [__Blueprint library__](bp_library.md)
— Blueprints provided to spawn actors.
-
- C++ reference
-
+ [__C++ reference__](ref_cpp.md)
— Classes and methods in CARLA C++.
-
- Recorder binary file format
-
+ [__Recorder binary file format__](ref_recorder_binary_file_format.md)
— Detailed explanation of the recorder file format.
-
- Sensors reference
-
+ [__Sensors reference__](ref_sensors.md)
— Everything about sensors and the data they retrieve.
## ROS bridge
-
- ROS bridge installation
-
- — How to install the ROS bridge's package or repository.
-
- CARLA messages reference
-
+ [__ROS bridge installation__](ros_installation.md)
+ — The different ways to install the ROS bridge.
+ [__CARLA messages reference__](ros_msgs.md)
— Contains explanations and fields for every type of CARLA message available in ROS.
-
- Launchfiles reference
-
- — Explains the launchfiles provided, its nodes, and the topics that are being consumed and published.
+ [__Launchfiles reference__](ros_launchs.md)
+ — Lists the launchfiles and nodes provided, and the topics being consumed and published.
+
## Tutorials — General
-
- Add friction triggers
-
+ [__Add friction triggers__](tuto_G_add_friction_triggers.md)
— Define dynamic box triggers for wheels.
-
- Control vehicle physics
-
+ [__Control vehicle physics__](tuto_G_control_vehicle_physics.md)
— Set runtime changes on a vehicle physics.
-
- Control walker skeletons
-
- — Skeleton and animation for walkers explained.
+ [__Control walker skeletons__](tuto_G_control_walker_skeletons.md)
+ — Animate walkers using skeletons.
+
## Tutorials — Assets
-
- Import new assets
-
+ [__Import new assets__](tuto_A_import_assets.md)
— Use personal assets in CARLA.
-
- Map creation
-
- — Guidelines to create a new map.
-
- Map customization
-
+ [__Map creation__](tuto_A_map_creation.md)
+ — Create a new map following simple guidelines.
+ [__Map customization__](tuto_A_map_customization.md)
— Edit an existing map.
-
- Standalone asset packages
-
- — Import assets into Unreal Engine and prepare them for package distribution.
-
- Use Epic's Automotive materials
-
- — Apply Epic's set of Automotive materials to vehicles for a more realistic painting.
-
- Vehicle modelling
-
- — Guidelines to create a new vehicle for CARLA.
+ [__Standalone asset packages__](tuto_A_standalone_packages.md)
+ — Import assets into UE and set them for package distribution.
+ [__Use Epic's Automotive materials__](tuto_A_epic_automotive_materials.md)
+ — Apply Epic's set of Automotive materials to vehicles.
+ [__Vehicle modelling__](tuto_A_vehicle_modelling.md)
+ — Create a new vehicle for CARLA.
+
## Tutorials — Developers
-
- Contribute with new assets
-
+ [__Contribute with new assets__](tuto_D_contribute_assets.md)
— Add new content to CARLA.
-
- Create a sensor
-
- — The basics on how to add a new sensor to CARLA.
-
- Make a release
-
+ [__Create a sensor__](tuto_D_create_sensor.md)
+ — Develop a new sensor to be used in CARLA.
+ [__Make a release__](tuto_D_make_release.md)
— For developers who want to publish a release.
-
- Generate pedestrian navigation
-
- — Generate the information needed for walkers to navigate a map.
+ [__Generate pedestrian navigation__](tuto_D_generate_pedestrian_navigation.md)
+ — Obtain the information needed for walkers to move around.
+
## Contributing
-
- Contribution guidelines
-
+ [__Contribution guidelines__](cont_contribution_guidelines.md)
— The different ways to contribute to CARLA.
-
- Code of conduct
-
- — Some standards for CARLA, rights and duties for contributors.
-
- Coding standard
-
+ [__Code of conduct__](cont_code_of_conduct.md)
+ — Standard rights and duties for contributors.
+ [__Coding standard__](cont_coding_standard.md)
— Guidelines to write proper code.
-
- Documentation standard
-
- — Guidelines to write proper documentation.
\ No newline at end of file
+ [__Documentation standard__](cont_doc_standard.md)
+ — Guidelines to write proper documentation.
+
\ No newline at end of file
diff --git a/Docs/start_introduction.md b/Docs/start_introduction.md
index 545a7863c..bf8d17719 100644
--- a/Docs/start_introduction.md
+++ b/Docs/start_introduction.md
@@ -21,12 +21,12 @@ The client side consists of a sum of client modules controlling the logic of act
That summarizes the basic structure of the simulator. Understanding CARLA though is much more than that, as many different features and elements coexist within it. Some of these are listed hereunder, as to gain perspective on the capabilities of what CARLA can achieve.
-* __Traffic manager:__ A built-in system that takes control of the vehicles besides the one used for learning. It acts as a conductor provided by CARLA to recreate urban-like environments with realistic behaviours.
-* __Sensors:__ Vehicles rely on them to dispense information of their surroundings. In CARLA they are a specific kind of actor attached the vehicle and the data they receive can be retrieved and stored to ease the process. Currently the project supports different types of these, from cameras to radars, lidar and many more.
-* __Recorder:__ This feature is used to reenact a simulation step by step for every actor in the world. It grants access to any moment in the timeline anywhere in the world, making for a great tracing tool.
-* __ROS bridge and Autoware implementation:__ As a matter of universalization, the CARLA project ties knots and works for the integration of the simulator within other learning environments.
-* __Open assets:__ CARLA facilitates different maps for urban settings with control over weather conditions and a blueprint library with a wide set of actors to be used. However, these elements can be customized and new can be generated following simple guidelines.
-* __Scenario runner:__ In order to ease the learning process for vehicles, CARLA provides a series of routes describing different situations to iterate on. These also set the basis for the [CARLA challenge](https://carlachallenge.org/), open for everybody to test their solutions and make it to the leaderboard.
+* __Traffic manager.__ A built-in system that takes control of the vehicles besides the one used for learning. It acts as a conductor provided by CARLA to recreate urban-like environments with realistic behaviours.
+* __Sensors.__ Vehicles rely on them to dispense information of their surroundings. In CARLA they are a specific kind of actor attached the vehicle and the data they receive can be retrieved and stored to ease the process. Currently the project supports different types of these, from cameras to radars, lidar and many more.
+* __Recorder.__ This feature is used to reenact a simulation step by step for every actor in the world. It grants access to any moment in the timeline anywhere in the world, making for a great tracing tool.
+* __ROS bridge and Autoware implementation.__ As a matter of universalization, the CARLA project ties knots and works for the integration of the simulator within other learning environments.
+* __Open assets.__ CARLA facilitates different maps for urban settings with control over weather conditions and a blueprint library with a wide set of actors to be used. However, these elements can be customized and new can be generated following simple guidelines.
+* __Scenario runner.__ In order to ease the learning process for vehicles, CARLA provides a series of routes describing different situations to iterate on. These also set the basis for the [CARLA challenge](https://carlachallenge.org/), open for everybody to test their solutions and make it to the leaderboard.
---
## The project
@@ -34,9 +34,9 @@ That summarizes the basic structure of the simulator. Understanding CARLA though
CARLA grows fast and steady, widening the range of solutions provided and opening the way for the different approaches to autonomous driving. It does so while never forgetting its open-source nature. The project is transparent, acting as a white box where anybody is granted access to the tools and the development community. In that democratization is where CARLA finds its value.
Talking about how CARLA grows means talking about a community of developers who dive together into the thorough question of autonomous driving. Everybody is free to explore with CARLA, find their own solutions and then share their achievements with the rest of the community.
-This documentation will be a companion along the way. The next page contains the __quickstart__ instructions for those eager to start. However, a build guide (Linux and Windows) is available for those who want to dive full-length into CARLA with all its features.
+This documentation will be a companion along the way. The next page contains __[Quick start](start_quickstart.md)__ instructions for those eager to install a CARLA release. There is also a build guide for Linux and Windows. This will make CARLA from repository and allow to dive full-length into its features.
-Welcome to CARLA.
+Welcome to CARLA.
diff --git a/Docs/start_quickstart.md b/Docs/start_quickstart.md
index 0f2c75845..00af21f38 100644
--- a/Docs/start_quickstart.md
+++ b/Docs/start_quickstart.md
@@ -1,50 +1,88 @@
# Quick start
- * [Requirements](#requirements)
- * [Downloading CARLA](#downloading-carla)
- * a) apt-get installation
- * b) Repository download
- * [Running CARLA](#running-carla)
+* __[Installation summary](#installation-summary)__
+* __[Requirements](#requirements)__
+* __[Downloading CARLA](#downloading-carla)__
+ * a) deb CARLA installation
+ * b) GitHub repository installation
+* __[Running CARLA](#running-carla)__
* Command-line options
- * [Updating CARLA](#updating-carla)
- * [Summary](#summary)
+* __[Updating CARLA](#updating-carla)__
+* __[Follow-up](#follow-up)__
+
+---
+## Installation summary
+
+
+
+ Show command line summary for the quick start installation
+
+
+```sh
+# Install required modules Pygame and Numpy.
+ pip install --user pygame numpy
+
+# Option A) deb package installation (only Linux)
+sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys DB53A429E64554FC &&
+sudo add-apt-repository "deb [trusted=yes] http://dist.carla.org/carla-0.9.7/ bionic main"
+sudo apt-get update
+sudo apt-get install carla
+cd /opt/carla/bin
+./CarlaUE4.sh
+
+# Option B) GitHub repository installation
+# Go to: https://github.com/carla-simulator/carla/blob/master/Docs/download.md
+# Download the desired package and additional assets.
+# Extract the package.
+# Extract the additional assets in `/Import`
+# Run CARLA (Linux)
+./CarlaUE.sh
+# Run CARLA (Windows)
+> CarlaUE4.exe
+
+# Run a script to test CARLA
+cd PythonAPI/examples
+python3 spawn_npc.py
+
+```
+
---
## Requirements
-The quickstart installation uses a pre-packaged version of CARLA. This comprises the content in a boundle that can run automatically with no build installation needed. The API can be accesseded fully but in exchange, advanced customization and developing options are unavailable.
-However, some requirements are still a must.
+The quick start installation uses a pre-packaged version of CARLA. The content is comprised in a boundle that can run automatically with no build installation needed. The API can be accesseded fully but advanced customization and development options are unavailable.
+The requirements are simpler than those for the build installation.
- * __Server side:__ A good GPU will be needed to run a highly realistic environment (4GB minimum). A dedicated GPU is highly advised for machine learning.
- * __Client side:__ The API is accessed via command line. To do so, [Python](https://www.python.org/downloads/) is necessary, and also a good internet connection and two TCP ports (2000 and 2001 by default).
- * __System requirements:__ Any 64-bits OS should run the corresponding version of CARLA.
- * __Other requirements:__ Only two specific Python modules: [Pygame](https://www.pygame.org/download.shtml), to create graphics directly with Python and [Numpy](https://pypi.org/project/numpy/) for great calculus.
+* __Server side.__ A 4GB minimum GPU will be needed to run a highly realistic environment. A dedicated GPU is highly advised for machine learning.
+* __Client side.__ [Python](https://www.python.org/downloads/) is necessary to access the API via command line. Also, a good internet connection and two TCP ports (2000 and 2001 by default).
+* __System requirements.__ Any 64-bits OS should run CARLA.
+* __Other requirements.__ Two Python modules: [Pygame](https://www.pygame.org/download.shtml) to create graphics directly with Python, and [Numpy](https://pypi.org/project/numpy/) for great calculus.
-If you have [pip](https://pip.pypa.io/en/stable/installing/) in your system, you can geth both modules simply by running the following commands:
+To install both modules using [pip](https://pip.pypa.io/en/stable/installing/), run the following commands.
```sh
pip install --user pygame numpy
```
---
## Downloading CARLA
-The easiest way to get the latest release in Linux is using the __apt repository__.
-To get either a specific release or get the Windows version of CARLA: __download the repository__.
-Both methods will set CARLA ready to run.
+The __deb installation__ is the easiest way to get the latest release in Linux.
+__Download the GitHub repository__ to get either a specific release or the Windows version of CARLA.
-
a) apt-get CARLA 0.9.7
+### a) deb CARLA installation
-First, add the repository to the system:
+Add the repository to the system.
```sh
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys DB53A429E64554FC &&
sudo add-apt-repository "deb [trusted=yes] http://dist.carla.org/carla-0.9.7/ bionic main"
```
-Then, simply install it. CARLA will be in the `/opt/` folder, where other software such as ROS install themselves:
+Install CARLA and check for the installation in the `/opt/` folder.
```sh
sudo apt-get update
sudo apt-get install carla
+cd /opt/carla
```
-
b) Downloading the repository
+### b) GitHub repository installation
-The repository contains the different versions of the simulator available. The _development_ and _stable_ sections, contain the packages for the different official releases. The later the version the more experimental it is. The _nightly build_ is the current development version as today and so, the most unstable (developers are currently working with this build). If you want a more robust version, you may search for the latest tagged version.
+The repository contains the different versions of the simulator available. _Development_ and _stable_ sections list the packages for the different official releases. The later the version the more experimental it is. The _nightly build_ is the current development version as today and so, the most unstable.
-!!! note
- Latest Windows release is __CARLA 0.9.5__, but this is to be updated soon.
+There may be many files per release. The package is a compressed file named as __CARLA_version.number__. Other elements such as __Town06_0.9.5.tar.gz__ are additional assets.
-There may be many files per release. The package is named as __CARLA_version.number__ (compressed file format _.tar.gz_ for Linux and _.zip_ for Windows). Other elements such as __Town06_0.9.5.tar.gz__ are additional assets for Linux releases.
-
-Download and extract the release in a folder of your choice. It contains a precompiled version of the simulator, the Python API module and some scripts to be used as examples.
-If you downloaded any additional assets in Linux, move them to the _Import_ folder that has appeared (there is no need to extract them). Open a terminal in the folder where you extracted CARLA and run the script _ImportAssets_ to get the additional content automatically:
+Download and extract the release file. It contains a precompiled version of the simulator, the Python API module and some scripts to be used as examples.
+Move any additional assets to the _Import_ folder in the release and extract them. The script _ImportAssets_ in the main CARLA folder should extract these automatically.
```sh
-./ImportAssets.sh
+> cd ~/carla
+> ./ImportAssets.sh
```
---
## Running CARLA
-Open a terminal in the folder where CARLA was extracted. The following command will execute the package file and start the simulation:
+Open a terminal in the main CARLA folder. Run the following command to execute the package file and start the simulation:
```sh
-Linux:
-./CarlaUE4.sh
-
-Windows:
-CarlaUE4.exe
+# Linux:
+> ./CarlaUE4.sh
+# Windows:
+> CarlaUE4.exe
```
!!! Important
- In the __apt-get__ installation, `CarlaUE4.sh` can be found in `/opt/carla/bin/`, instead of the main `/carla/` folder where it normally is.
+ In the __deb installation__, `CarlaUE4.sh` will be in `/opt/carla/bin/`, instead of the main `/carla/` folder where it normally is.
-A window will open, containing a view over the city. This is the "spectator" view. To fly around the city use the mouse and WASD keys (while clicking). The simulator is now running as a server, waiting for a client app to connect and interact with the world.
+A window containing a view over the city will pop up. This is the _spectator view_. To fly around the city use the mouse and `WASD` keys (while clicking). The server simulator is now running and waiting for a client to connect and interact with the world.
+Now it is time to start running scripts. The following example will spawn some life into the city:
-!!! note
- If the firewall or any other application are blocking the TCP ports needed, these can be manually changed by adding to the previous command the argument: `-carla-port=N`, being `N` the desired port. The second will be automatically set to `N+1`.
+```sh
+# Go to the folder containing example scripts
+> cd PythonAPI/examples
+> python3 spawn_npc.py
+```
#### Command-line options
-There are some configuration options available when launching CARLA:
+There are some configuration options available when launching CARLA.
- * `-carla-rpc-port=N` Listen for client connections at port N, streaming port is set to N+1 by default.
- * `-carla-streaming-port=N` Specify the port for sensor data streaming, use 0 to get a random unused port.
- * `-quality-level={Low,Epic}` Change graphics quality level.
- * [Full list of UE4 command-line arguments][ue4clilink] (note that many of these won't work in the release version).
+* `-carla-rpc-port=N` Listen for client connections at port `N`. Streaming port is set to `N+1` by default.
+* `-carla-streaming-port=N` Specify the port for sensor data streaming. Use 0 to get a random unused port. The second port will be automatically set to `N+1`.
+* `-quality-level={Low,Epic}` Change graphics quality level. Find out more in [rendering options](adv_rendering_options.md).
+* __[Full list of UE4 command-line arguments][ue4clilink].__ There is a lot of options provided by UE. However, not all of these will be available in CARLA.
[ue4clilink]: https://docs.unrealengine.com/en-US/Programming/Basics/CommandLineArguments
```sh
> ./CarlaUE4.sh -carla-rpc-port=3000
```
-However, some may not be available (especially those provided by UE). For said reason, the script in `PythonAPI/util/config.py` provides for some more configuration options:
+The script `PythonAPI/util/config.py` provides for more configuration options.
+
```sh
> ./config.py --no-rendering # Disable rendering
> ./config.py --map Town05 # Change map
> ./config.py --weather ClearNoon # Change weather
-```
-To check all the available configurations, run the following command:
-
-```sh
-> ./config.py --help
+> ./config.py --help # Check all the available configuration options.
```
---
## Updating CARLA
-The packaged version requires no updates. The content is bundled and thus, tied to a specific version of CARLA. Everytime there is a release, the repository will be updated. To run this latest or any other version, delete the previous one and repeat the installation steps with the desired.
+The packaged version requires no updates. The content is bundled and thus, tied to a specific version of CARLA. Everytime there is a release, the repository will be updated. To run this latest or any other version, delete the previous and install the one desired.
---
-## Summary
+## Follow-up
-That concludes the quickstart installation process. In case any unexpected error or issue occurs, the [CARLA forum](https://forum.carla.org/) is open to everybody. There is an _Installation issues_ category to post this kind of problems and doubts.
+Thus concludes the quick start installation process. In case any unexpected error or issue occurs, the [CARLA forum](https://forum.carla.org/) is open to everybody. There is an _Installation issues_ category to post this kind of problems and doubts.
-So far, CARLA should be operative in the desired system. Terminals will be used to contact the server via script and retrieve data. Thus will access all of the capabilities that CARLA provides. Next step should be visiting the __First steps__ section to learn more about this. However, all the information about the Python API regarding classes and its methods can be accessed in the [Python API reference](python_api.md).
+So far, CARLA should be operative in the desired system. Terminals will be used to contact the server via script, interact with the simulation and retrieve data. To do so, it is essential to understand the core concepts in CARLA. Read the __First steps__ section to learn on those. Additionally, all the information about the Python API regarding classes and its methods can be accessed in the [Python API reference](python_api.md).