Merge pull request #108 from ucla-mobility/develop

.travis.yml

@@ -7,10 +7,7 @@ python:
 
 install:
  - pip install --upgrade pip
- - pip install -r requirements_ci.txt
- - pip install pytest
- - pip install pytest-cov
- - pip install coveralls
+ - pip install coveralls pytest pytest-cov -r requirements_ci.txt
 
 # command to run tests
 script:
@@ -24,4 +21,4 @@ notifications:
  recipients:
  - derrickxu1994@gmail.com
  on_success: never # default: change
- on_failure: always # default: always
+ on_failure: always # default: always

README.md

@@ -4,22 +4,20 @@
 [![Documentation Status](https://readthedocs.org/projects/opencda-documentation/badge/?version=latest)](https://opencda-documentation.readthedocs.io/en/latest/?badge=latest)
 
 
+OpenCDA is a <strong>SIMULATION</strong> tool integrated with a prototype cooperative driving automation (CDA; see [SAE J3216](https://www.sae.org/standards/content/j3216_202005/)) pipeline as
+well as regular automated driving components (e.g., perception, localization, planning, control). The tool integrates automated driving simulation (CARLA), traffic simulation (SUMO), and Co-simulation (CARLA + SUMO). 
 
-OpenCDA is a generalized framework for prototyping <strong>cooperative driving automation (CDA; see [SAE J3216](](https://www.sae.org/standards/content/j3216_202005/)))
-applications</strong> (e.g., cooperative perception, platooning) as well as <strong>automated driving components</strong> (e.g., 
-perception, localization, planning, control) integrated with automated driving simulation (CARLA), traffic simulation (SUMO), and <strong>Co-simulation</strong> (CARLA + SUMO). OpenCDA builds upon standard automated driving system (ADS) platforms and focuses on various classes of data exchanges and cooperation between vehicles, infrastructure, and other road users (e.g., pedestrians).
-
-The goal of the OpenCDA project is to enable researchers and engineers to prototype any cooperative driving applications using our full-stack CDA platform and test them from both automated driving and traffic operations perspectives. OpenCDA bridges the gaps between the communities of automated driving and transporation engineering. Eventually, OpenCDA can be used to develop and test cooperative driving functions and applications for improving safety, sustainability, and efficiency of future transportation systems.
+OpenCDA is <strong>all in Python</strong>. The purpose is to enable researchers to fast-prototype, simulate, and test CDA algorithms and functions. By applying our simulation tool, users can conveniently conduct both task-specific evaluation (e.g. object detection accuracy) and pipeline-level assessment (e.g. traffic safety) on their customized algorithms.
 
+Inspired by the [USDOT CDA CARMA program](https://its.dot.gov/cda/), OpenCDA, as an open-source project, makes a unique contribution from the perspective of initial-stage development and testing using simulation. 
+Users can consider using OpenCDA for <strong>initial algorithmic testing</strong>, and should use the [CARMA everything-in-the-loop](https://github.com/usdot-fhwa-stol/carma-simulation) evaluation tool and [CARMA platform](https://github.com/usdot-fhwa-stol/carma-platform) for software platform development and field testing.
 
 The key features of OpenCDA are:
-* <strong>Full-stack CDA System</strong>: OpenCDA provides a full-stack automated driving and cooperative driving software system that contains perception, localization, planning, control, and V2X communication modules. 
-* <strong>Connectivity and Cooperation</strong>: OpenCDA supports various levels and categories of cooperation between CAVs in simulation.
- This differentiates OpenCDA with other single vehicle automation tools.
-* <strong>Integration</strong>: OpenCDA utilizes CARLA and SUMO separately, as well as integrates them together for realistic scene rendering, vehicle modeling, and traffic simulation. It is also efficient and flexible enough to be applied for software-in-the-loop and hardware-in-the-loop testing.
+* <strong>Integration</strong>: OpenCDA utilizes CARLA and SUMO separately, as well as integrates them together for realistic scene rendering, vehicle modeling, and traffic simulation.
+* <strong> Full-stack</strong> prototype CDA Platform in Simulation: OpenCDA provides a simple prototype automated driving and cooperative driving platform, all in Python, that contains perception, localization, planning, control, and V2X communication modules.
 * <strong>Modularity</strong>: OpenCDA is highly modularized, enabling users to conveniently replace any default algorithms or protocols with their own customzied design. 
 * <strong>Benchmark</strong>: OpenCDA offers benchmark testing scenarios, benchmark baseline maps, state-of-the-art benchmark algorithms for ADS and Cooperative ADS functions, and benchmark evaluation metrics.
-
+* <strong>Connectivity and Cooperation</strong>: OpenCDA supports various levels and categories of cooperation between CAVs in simulation. This differentiates OpenCDA from other single vehicle simulation tools.
 
 
 Users could refer to [OpenCDA documentation](https://opencda-documentation.readthedocs.io/en/latest/) for more details.

docs/index.rst

@@ -5,9 +5,11 @@
 
 Welcome to OpenCDA's documentation!
 ===================================
-OpenCDA is a generalized framework for prototyping cooperative driving automation (CDA; see SAE J3216) applications (e.g., cooperative perception, platooning) as well as automated driving components (e.g., perception, localization, planning, control) integrated with automated driving simulation (CARLA), traffic simulation (SUMO), and Co-simulation (CARLA + SUMO). OpenCDA builds upon standard automated driving system (ADS) platforms and focuses on various classes of data exchanges and cooperation between vehicles, infrastructure, and other road users (e.g., pedestrians).
+OpenCDA is a **SIMULATION** tool integrated with a prototype cooperative driving automation (CDA; see SAE J3216) pipeline as
+well as regular automated driving components (e.g., perception, localization, planning, control). The tool integrates automated driving simulation (CARLA), traffic simulation (SUMO), and Co-simulation (CARLA + SUMO).
+OpenCDA builds upon standard automated driving system (ADS) platforms and focuses on various classes of data exchanges and cooperation between vehicles, infrastructure, and other road users (e.g., pedestrians).
 
-The goal of the OpenCDA project is to enable researchers and engineers to prototype any cooperative driving applications using our full-stack CDA platform and test them from both automated driving and traffic operations perspectives. OpenCDA bridges the gaps between the communities of automated driving and transporation engineering. Eventually, OpenCDA can be used to develop and test cooperative driving functions and applications for improving safety, sustainability, and efficiency of future transportation systems.
+OpenCDA is **all in Python**. The purpose is to enable researchers to fast-prototype, simulate, and test CDA algorithms and functions. By applying our simulation tool, users can conveniently conduct both task-specific evaluation (e.g. object detection accuracy) and pipeline-level assessment (e.g. traffic safety) on their customized algorithms.
 
 OpenCDA is a work in progress. Many features on the roadmap are being continuously developed. We welcome your contribution and please visit our Github repo
 for the latest release.
@@ -47,15 +49,14 @@ for the latest release.
 
 **Citing OpenCDA**\ :
 
-If you are using our OpenCDA framework or codes for your development, please cite the following paper:
+If you are using our OpenCDA framework or codes for your development, please cite the following paper::
 
  @inproceedings{xu2021opencda,
- author  = {Runsheng Xu, Yi Guo, Xu Han, Xin Xia, Hao Xiang, Jiaqi Ma},
- title  = {OpenCDA: An Open Cooperative Driving Automation
+ author = {Runsheng Xu, Yi Guo, Xu Han, Xin Xia, Hao Xiang, Jiaqi Ma},
+ title = {OpenCDA: An Open Cooperative Driving Automation
  Framework Integrated with Co-Simulation},
  booktitle = {2021 IEEE Intelligent Transportation Systems Conference (ITSC)},
- year = {2021}
- }
+ year = {2021}}
 
 Our paper can be accessed by arxiv: https://arxiv.org/abs/2107.06260
 

docs/md_files/introduction.md

@@ -28,10 +28,9 @@ OpenCDA is created to fill such gaps.
 
 ### Key Features
 The key features of OpenCDA are:
-* <strong>Full-stack CDA System</strong>: OpenCDA provides a full-stack automated driving and cooperative driving software system that contains perception, localization, planning, control, and V2X communication modules.
-* <strong>Connectivity and Cooperation</strong>: OpenCDA supports various levels and categories of cooperation between CAVs in simulation. This differentiates OpenCDA with other single vehicle automation tools.
- This differentiates OpenCDA with other single vehicle automation tools.
-* <strong>Integration</strong>: OpenCDA utilizes CARLA and SUMO separately, as well as integrates them together for realistic scene rendering, vehicle modeling, and traffic simulation. It is also efficient and flexible enough to be applied for software-in-the-loop and hardware-in-the-loop testing.
+* <strong>Integration</strong>: OpenCDA utilizes CARLA and SUMO separately, as well as integrates them together for realistic scene rendering, vehicle modeling, and traffic simulation.
+* <strong> Full-stack</strong> prototype CDA Platform in Simulation: OpenCDA provides a simple prototype automated driving and cooperative driving platform, all in Python, that contains perception, localization, planning, control, and V2X communication modules.
 * <strong>Modularity</strong>: OpenCDA is highly modularized, enabling users to conveniently replace any default algorithms or protocols with their own customzied design. 
-* <strong>Benchmark</strong>: OOpenCDA offers benchmark testing scenarios, benchmark baseline maps, state-of-the-art benchmark algorithms for ADS and Cooperative ADS functions, and benchmark evaluation metrics.
+* <strong>Benchmark</strong>: OpenCDA offers benchmark testing scenarios, benchmark baseline maps, state-of-the-art benchmark algorithms for ADS and Cooperative ADS functions, and benchmark evaluation metrics.
+* <strong>Connectivity and Cooperation</strong>: OpenCDA supports various levels and categories of cooperation between CAVs in simulation. This differentiates OpenCDA from other single vehicle simulation tools.
 

docs/md_files/traffic_generation.md

@@ -82,6 +82,6 @@ To generate the Sumo traffic flow, three things are needed:
  sumo_file_parent_path=sumo_cfg)
 
  while True:
- scenario_manager.tick()
+  scenario_manager.tick()
  ```