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Copy file name to clipboardexpand all lines: README.md
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Users could refer to [OpenCDA documentation](https://opencda-documentation.readthedocs.io/en/latest/) for more details.
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## What's New
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### March 2022
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* HD Map manager is online! It currently supports generating rasterization map that includes road topology, traffic light info, and dynamic objects around each cav in real-time. This can be
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used for RL planning, HD Map learning, scene understanding, etc.
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### Feb 2022
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* Our paper [*OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication*](https://arxiv.org/abs/2109.07644)
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has been accepted by ICRA 2022! It utilizes the offline Cooperative Perception(data dumping) function in OpenCDA. Check the benchmark codebase [OpenCOOD](https://github.com/DerrickXuNu/OpenCOOD) of OPV2V if interested.
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### Oct 2021
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* CARLA 0.9.12 now supported; Different weather conditions are supported.
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* Better traffic management supported: users now can set a customized range to background cars.
Please use [this template](.github/PR_TEMPLATE.md) for your pull requests.
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## What's New
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### Feb 2022
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* Our paper [*OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication*](https://arxiv.org/abs/2109.07644)
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has been accepted by ICRA 2022! It utilizes the offline Cooperative Perception(data dumping) function in OpenCDA. Check the [github page](https://github.com/DerrickXuNu/OpenCOOD) of OPV2V if interested.
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### Oct 2021
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* CARLA 0.9.12 now supported; Different weather conditions are supported.
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* Better traffic management supported: users now can set a customized range to background cars.
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## Citation
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If you are using our OpenCDA framework or codes for your development, please cite the following paper:
Copy file name to clipboardexpand all lines: docs/md_files/release_history.md
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## Release History of OpenCDA
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This page demonstrates all the changes since the origin release v0.1.0 with more detailed imags.
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---
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### v0.1.2
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##### Map manager
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OpenCDA now adds a new component `map_manager` for each cav. It will dynamically load road topology, traffic light information, and dynamic
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objects information around the ego vehicle and save them into rasterized map, which can be useful for RL planning, HDMap learning, scene understanding, etc.
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Key elements in the rasterization map:
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- Drivable space colored by black
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- Lanes
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- Red lane: the lanes that are controlled by red traffic light
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- Green lane: the lanes that are controlled by green traffic light
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- Yellow lane: the lanes that are not effected by any traffic light
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- Objects that are colored by white and represented as rectangle
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