Merge pull request #94 from ucla-mobility/feature/intro_review

Feature/intro review

LICENSE

@@ -1,21 +1,10 @@
-MIT License
 
-Copyright (c) 2021 UCLA Mobility Lab
+Academic Software License: © 2021 UCLA Mobility Lab (“Institution”).  Academic or nonprofit researchers are permitted to use this Software (as defined below) subject to Paragraphs 1-3:
 
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
+Institution hereby grants to you free of charge, so long as you are an academic or nonprofit researcher, a nonexclusive license under Institution’s copyright ownership interest in this software and any derivative works made by you thereof (collectively, the “Software”) to use, copy, and make derivative works of the Software solely for educational or academic research purposes, in all cases subject to the terms of this Academic Software License. Except as granted herein, all rights are reserved by Institution, including the right to pursue patent protection of the Software.
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+Please note you are prohibited from further transferring the Software -- including any derivatives you make thereof -- to any person or entity. Failure by you to adhere to the requirements in Paragraphs 1 and 2 will result in immediate termination of the license granted to you pursuant to this Academic Software License effective as of the date you first used the Software.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+IN NO EVENT SHALL INSTITUTION BE LIABLE TO ANY ENTITY OR PERSON FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE, EVEN IF INSTITUTION HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. INSTITUTION SPECIFICALLY DISCLAIMS ANY AND ALL WARRANTIES, EXPRESS AND IMPLIED, INCLUDING, BUT NOT LIMITED TO, ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE IS PROVIDED “AS IS.” INSTITUTION HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS OF THIS SOFTWARE.
+
+Commercial entities: please contact the UCLA Mobility Lab at jiaqima@ucla.edu for licensing opportunities.

README.md

@@ -5,32 +5,49 @@
 
 
 
-OpenCDA is a generalized framework for fast prototyping <strong>cooperative driving automation 
-applications</strong>(e.g., cooperative perception, platooning) as well as <strong>autonomous vehicle components</strong>(e.g., 
-perception, localization, planning, control) under <strong>Co-simulation</strong>(CARLA and 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.
 
 
-The key features of OpenCDA can be summarized as <strong>CIFMB</strong>:
-* <strong>C</strong>onnectivity and <strong>C</strong>ooperation: OpenCDA supports various levels and categories of cooperation between CAVs in simulation.
+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>I</strong>ntegration: OpenCDA integrates CARLA and SUMO together for realistic scene rendering, vehicle modeling and traffic simulation.
-* <strong>F</strong>ull-stack System: OpenCDA provides a full-stack software system that contains perception, localization, planning, control, and V2X communication modules.
-* <strong>M</strong>odularity: OpenCDA is highly modularized, enabling users to conveniently replace any default algorithms or protocols with their own customzied design. 
-* <strong>B</strong>enchmark: OpenCDA offers benchmark testing scenarios, state-of-the-art benchmark algorithms, and benchmark evaluation metrics.
+* <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>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.
 
 
 
-Users could refer to [OpenCDA documentation](https://opencda-documentation.readthedocs.io/en/latest/) to see more details.
+Users could refer to [OpenCDA documentation](https://opencda-documentation.readthedocs.io/en/latest/) for more details.
 
 ## Major Components
 ![teaser](docs/md_files/images/OpenCDA_diagrams.png )
 
-OpenCDA  is composed of three major component: <strong>Cooperative Driving System</strong>,  <strong>Co-Simulation Tools</strong>,
+OpenCDA  consists of three major component: <strong>Cooperative Driving System</strong>,  <strong>Co-Simulation Tools</strong>,
 and  <strong>Scenario Manager</strong>.
 
 Check the [OpenCDA Introduction](https://opencda-documentation.readthedocs.io/en/latest/md_files/introduction.html) for more details.
 
+
+ ## Citation
+ If you are using our OpenCDA framework or codes for your development, please cite the following paper:
+ ```bibtex
+@inproceedings{xu2021opencda,
+title={OpenCDA:  An  Open  Cooperative  Driving  Automation  Framework
+Integrated  with  Co-Simulation},
+author={Runsheng Xu, Yi Guo, Xu Han, Xin Xia, Hao Xiang, Jiaqi Ma},
+booktitle={2021 IEEE Intelligent Transportation Systems Conference (ITSC)},
+year={2021}
+}
+```
+The arxiv link to the paper:  https://arxiv.org/abs/2107.06260
+
+Also, under this LICENSE, OpenCDA is for non-commercial research only. Researchers to be able to modify the source code for their own research only. Contracted work that generates corporate revenues and other general commecial use are prohibited under this lICENSE. See the LICENSE file for details and possible opportunities for commercial use.
+
 ## Get Started
 
  ![teaser](docs/md_files/images/platoon_joining_2lanefree_complete.gif)
@@ -44,8 +61,8 @@ Check the [OpenCDA Introduction](https://opencda-documentation.readthedocs.io/en
 * [Traffic Generation](https://opencda-documentation.readthedocs.io/en/latest/md_files/traffic_generation.html)
 
 
-Note: We are keeping improving the performance of OpenCDA. Currently, it is mainly tested in our 2-lane highway customized map and
- Carla town06 map, therefore, we <strong>DO NOT </strong> guarantee the same level of  robustness in other maps.
+Note: We continuously improve the performance of OpenCDA. Currently, it is mainly tested in our customized maps and
+ Carla town06 map; therefore, we <strong>DO NOT </strong> guarantee the same level of  robustness in other maps.
 
 ### Developer Guide
 
@@ -60,38 +77,39 @@ We welcome your contributions.
 - 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.
 
- ## Citation
- If you are using our framework for your development, please cite the following paper:
- ```bibtex
-@inproceedings{xu2021opencda,
-title={OpenCDA:  An  Open  Cooperative  Driving  Automation  Framework
-Integrated  with  Co-Simulation},
-author={Runsheng Xu, Yi Guo, Xu Han, Xin Xia, Hao Xiang, Jiaqi Ma},
-booktitle={2021 IEEE Intelligent Transportation Systems Conference (ITSC)},
-year={2021}
-}
-```
-And the arxiv link to the paper:  https://arxiv.org/abs/2107.06260
-
-## 2021 RoadMap
+## 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)
+* CARLA only simulation
+* Co-Simulation function with CARLA + SUMO
+* Scenario manager and scenario database for CDA freeway applications
+
+
+## In Future Releases
+Future versions are expected to include the following:
+* OpenCDA v0.2.0 and above software stack, including signalized intersection and corridor applications, cooperative perception and localization, enhanced scenario generation/manager and scenario database for newly added CDA applications)
+* SUMO only simulation which includes SUMO impplementation of all cooperative driving applications using behavior based approach (consistent with CARLA implementation)
+* Software-in-the-loop interfaces with two open-source ADS platforms, i.e., Autoware and CARMA
+* hardware-in-the-loop interfaces and example projects with a real automated driving vehicle platform and a driving simulator
+
+<!-- ## 2021 RoadMap
 ![teaser](docs/md_files/images/roadmap.PNG)
+-->
 
 ## Contributors
 OpenCDA is supported by the [UCLA Mobility Lab](https://mobility-lab.seas.ucla.edu/). <br>
 
+### Lab Principal Investigator:
+- Dr. Jiaqi Ma ([linkedin](https://www.linkedin.com/in/jiaqi-ma-17037838/),
+               [UCLA Samueli](https://samueli.ucla.edu/people/jiaqi-ma/))
 
-### Project Leader: <br>
- Runsheng Xu ([linkedin](https://www.linkedin.com/in/runsheng-xu/), [github](https://github.com/DerrickXuNu))  <br>
+### Project Lead: <br>
+ - Runsheng Xu ([linkedin](https://www.linkedin.com/in/runsheng-xu/), [github](https://github.com/DerrickXuNu))  <br>
 
-### Major developers: 
- - Runsheng Xu([linkedin](https://www.linkedin.com/in/runsheng-xu/), [github](https://github.com/DerrickXuNu))
- - 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))
-
-### Theoretical supports:
- - Dr. Yi Guo([linkedin](https://www.linkedin.com/in/yi-guo-4008baaa/))
- - Dr. Xin Xia([linkedin](https://www.linkedin.com/in/yi-guo-4008baaa/))
+### 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/))
 
-### Lab Supervisor
-- Dr. Jiaqi Ma([linkedin](https://www.linkedin.com/in/jiaqi-ma-17037838/),
-               [google scholar](https://scholar.google.com/citations?user=S3cQz1AAAAAJ&hl=en))
+