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*`-t`: The name of the tesitng scenario. A python script with the same name should exist in
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`opencda/scenario_testing/` to construct the simulation, and a yaml file with the same name should exist in
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`opencda/scenario_testing/config_yaml/` to define the simulation parameters.
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*`-v --version`: Define the CARLA simulator version, default 0.9.11. Right now only 0.9.11 and 0.9.12 are supported.
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We need this as there are some api changes from 0.9.11 to 0.9.12.
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*`--apply_ml` (Optional): A flag to indicate whether a deep learning model needs to be loaded. If this flag is
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set, Pytorch will be imported.
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*`--record` (Optional): A flag to indicate whether to record this simulation. [Check here for more details](https://carla.readthedocs.io/en/latest/adv_recorder/).
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---
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### Single Vehicle Test
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**Notice: <strong> Change -v 0.9.11 to -v 0.9.12 if you are using CARLA 0.9.12.</strong>
The apply_ml flag will import the pytorch library and load Yolov5 model (<strong>Thus Pytorch is required</strong>) for object detection. Thus, in this
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scenario, the <strong>perception</strong>, localization, planning and control modules will all be activated.
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#### 3. Town06 Co-simulation test (Pytorch and Sumo required)
Copy file name to clipboardexpand all lines: docs/md_files/installation.md
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To get started, the following requirements should be fulfilled.
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*__System requirements.__ Any 64-bits OS should run OpenCDA. We highly recommends Ubuntu 16.04/18.04/20.04.
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*__Adequate GPU.__ CARLA is a realistic simulation platform based on Unity, which requires at least a 3GB GPU for smooth scene rendering, though 8GB is recommended.
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*__Adequate GPU.__ CARLA is a realistic simulation platform based on Unreal Engine 4, which requires at least a 3GB GPU for smooth scene rendering, though 8GB is recommended.
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*__Disk Space.__ Estimate 100GB of space is recommended to install CARLA and Unreal Engine.
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*__Python__ Python3.7 or higher version is required for full functions.
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Pytorch (optional). Pytorch is required only when you want to activate the perception module; otherwise OpenCDA
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will retrieve all perception information from the simulation server directly.
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### 1. CARLA Installation (0.9.11 required)
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### 1. CARLA Installation (>= 0.9.11 required)
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There are two different recommended ways to install the CARLA simulator and either way is fine for using OpenCDA. <br>
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Note: If you want to use the customized highway map with full assets (.fbx, .xml and .xodr) in OpenCDA,
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<divclass="build-buttons">
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<p>
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<ahref="https://github.com/carla-simulator/carla/releases/tag/0.9.11"target="_blank"class="btn btn-neutral"title="Go to the latest CARLA release">
<ahref="https://github.com/carla-simulator/carla/releases"target="_blank"class="btn btn-neutral"title="Go to the latest CARLA release">
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<spanclass="icon icon-github"></span> CARLA Released Package</a>
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</p>
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</div>
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OpenCDA is only tested at 0.9.11 and thus we highly recommend using this version.
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OpenCDA is tested both at 0.9.11 and 0.9.12.
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To install CARLA as a precompiled package, 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. <br>
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<strong>Note: The AdditionalMaps_0.9.11.tar.gz also need to be downloaded and extract to the CARLA repo to support
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<strong>Note: The AdditionalMaps_0.9.1x.tar.gz also need to be downloaded and extract to the CARLA repo to support
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scenario testings in Town06.</strong>
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#### 1.2 Build From Source
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You can do this by running this script:
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```sh
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export CARLA_HOME=/path/to/your/CARLA_ROOT
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export CARLA_VERSION=0.9.11 #or 0.9.12 depends on your CARLA
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. setup.sh
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```
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If everything works correctly, you will see a cache folder is created in your OpenCDA root dir, and the terminal shows
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```sh
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python -c "import carla"# check whether carla is installed correctly.
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```
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<strong>Note: If you are using Python other than 3.7 and CARLA rather than 0.9.11, then you have to change the setup.sh to your
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<strong>Note: If you are using Python other than 3.7 and CARLA < 0.9.11 then you have to change the setup.sh to your
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carla version's egg file or manually installed carla to your conda environment.</strong>
Besides the above dumped data, users can also generate the future trajectory for each
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vehicle for trajectory prediction purpose. Run `python root_of_opencda/scripts/generate_prediction_yaml.py`
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to generate the prediction offline.
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This new functionality has been proved helpful. The newest paper <strong>OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication</strong>
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has utilized this new feature to collect cooperative data. Check https://mobility-lab.seas.ucla.edu/opv2v/ for more information
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#### CARLA 0.9.12 Support
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OpenCDA now supports both CARLA 0.9.12 and 0.9.11. Users needs to set CARLA_VERSION variable before
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installing OpenCDA. When users run opencda.py, -v argument is required to classify the CARLA version for
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OpenCDA to select the correct API.
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#### Weather Parameters
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To help estimate the influence of weather on cooperative driving automation, users now can
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define weather setting in the yaml file to control sunlight, fog, rain, wetness and other conditions.
Copy file name to clipboardexpand all lines: docs/md_files/yaml_define.md
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sync_mode: true # whether to use sync mode
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client_port: 2000# client port to connect to the server
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fixed_delta_seconds: &delta 0.05 # fixed time step
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weather: # set weather parameters
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sun_altitude_angle: 15# 90 is the midday and -90 is the midnight
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cloudiness: 0# 0 is the clean sky and 100 is the thickest cloud
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precipitation: 0# rain, 100 is the heaviest rain
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precipitation_deposits: 0# Determines the creation of puddles. Values range from 0 to 100, being 0 none at all and 100 a road completely capped with water.
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wind_intensity: 0# it will influence the rain
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fog_density: 0# fog thickness, 100 is the largest
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fog_distance: 0# Fog start distance. Values range from 0 to infinite.
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fog_falloff: 0# Density of the fog (as in specific mass) from 0 to infinity. The bigger the value, the more dense and heavy it will be, and the fog will reach smaller heights
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