Merge pull request #114 from ucla-mobility/develop

Update project description.

README.md

@@ -9,8 +9,7 @@ well as regular automated driving components (e.g., perception, localization, pl
 
 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.
+In collaboration with [U.S.DOT CDA Research](https://its.dot.gov/cda/) and the [FHWA CARMA Program](https://highways.dot.gov/research/operations/CARMA), OpenCDA, as an open-source project, makes a unique contribution from the perspective of initial-stage development and testing using simulation. OpenCDA is designed and built to support <strong>initial algorithmic testing</strong> for CDA Features. Through collaboration with CARMA Collaborative, this tool provides a unique capability to the CDA research community and will interface with the [CARMA XiL tools](https://github.com/usdot-fhwa-stol/carma-simulation) being developed by U.S.DOT to support more advanced simulation testing of CDA Features.
 
 The key features of OpenCDA are:
 * <strong>Integration</strong>: OpenCDA utilizes CARLA and SUMO separately, as well as integrates them together for realistic scene rendering, vehicle modeling, and traffic simulation.
@@ -19,7 +18,7 @@ The key features of OpenCDA are:
 * <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.
 
 ## Major Components
@@ -73,7 +72,7 @@ We welcome your contributions.
 - Please report bugs and improvements by submitting issues.
 - Submit your contributions using [pull requests](https://github.com/ucla-mobility/OpenCDA/pulls).
  Please use [this template](.github/PR_TEMPLATE.md) for your pull requests.
- 
+
 ## In OpenCDA v0.1.0 Release
 The current version features the following:
 * OpenCDA v0.1.0 software stack (basic ADS and cooperative ADS platform, benchmark algorithms for platooning, cooperative lane change, merge, and other freeway maneuvers)
@@ -102,11 +101,10 @@ OpenCDA is supported by the [UCLA Mobility Lab](https://mobility-lab.seas.ucla.e
 
 ### Project Lead: <br>
  - Runsheng Xu ([linkedin](https://www.linkedin.com/in/runsheng-xu/), [github](https://github.com/DerrickXuNu)) <br>
- 
+
 ### Team Members: 
  - Xu Han ([linkedin](https://linkedin.com/in/xu-han-12851a64), [github](https://github.com/xuhan417))
  - Hao Xiang ([linkedin](https://www.linkedin.com/in/hao-xiang-42bb5a1b2/), [github](https://github.com/XHwind))
  - Dr. Yi Guo ([linkedin](https://www.linkedin.com/in/yi-guo-4008baaa/))
  - Dr. Xin Xia ([linkedin](https://www.linkedin.com/in/yi-guo-4008baaa/))
-
 

docs/index.rst

@@ -11,6 +11,8 @@ OpenCDA builds upon standard automated driving system (ADS) platforms and focuse
 
 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.
 
+In collaboration with `U.S.DOT CDA Research <https://its.dot.gov/cda/>`_ and the `FHWA CARMA Program <https://highways.dot.gov/research/operations/CARMA>`_, OpenCDA, as an open-source project, makes a unique contribution from the perspective of initial-stage development and testing using simulation. OpenCDA is designed and built to support **initial algorithmic testing** for CDA Features. Through collaboration with CARMA Collaborative, this tool provides a unique capability to the CDA research community and will interface with the `CARMA XiL tools <https://github.com/usdot-fhwa-stol/carma-simulation>`_ being developed by U.S.DOT to support more advanced simulation testing of CDA Features.
+
 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.