Merge pull request #97 from ucla-mobility/develop

Develop

README.md

@@ -46,8 +46,7 @@ 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.
-
+Also, under this LICENSE, OpenCDA is for non-commercial research only. Researchers can modify the source code for their own research only. Contracted work that generates corporate revenues and other general commercial 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)

docs/index.rst

@@ -5,14 +5,12 @@
 
 Welcome to OpenCDA's documentation!
 ===================================
-OpenCDA is a generalized framework for fast developing and testing
-cooperative driving automation applications(e.g., cooperative
-perception, platooning) as well as autonomous vehicle components(e.g.,
-perception, localization, planning, control) under Co-simulation(CARLA-SUMO).
+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 work in progress, and many features are still in the future
-roadmap. We welcome your contribution and please visit our Github repo
-for latest release.
+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 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.
 
     `OpenCDA source code on  Github <https://github.com/ucla-mobility/OpenCDA>`_
 
@@ -49,8 +47,7 @@ for latest release.
 
 **Citing OpenCDA**\ :
 
-If you use OpenCDA in a scientific publication, we would appreciate
-citations to 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},
@@ -62,6 +59,8 @@ citations to the following paper::
 
 Our paper can be accessed by arxiv: https://arxiv.org/abs/2107.06260
 
+Also, under this LICENSE, OpenCDA is for non-commercial research only. Researchers can modify the source code for their own research only. Contracted work that generates corporate revenues and other general commercial use are prohibited under this LICENSE. See the LICENSE file for details and possible opportunities for commercial use.
+
 Indices and tables
 ==================
 

docs/md_files/contributor.md

@@ -1,32 +1,33 @@
-## About us
-OpenCDA is funded and mainly support by [UCLA Mobility Lab](https://mobility-lab.seas.ucla.edu/).
+## About Us
+OpenCDA is brought to you by [UCLA Mobility Lab](https://mobility-lab.seas.ucla.edu/).
+
+### Supervisor
+Dr. Jiaqi Ma (Associate Professor @ UCLA)
+- Lab Principal Investigator
+- [Linkedin](https://www.linkedin.com/in/jiaqi-ma-17037838/)
+- [Google Scholar](https://scholar.google.com/citations?user=S3cQz1AAAAAJ&hl=en)
+
 
 ### Core Developer
 Runsheng Xu (Ph.D. Student @ UCLA):
-- OpenCDA System Architect
+- Project Lead and OpenCDA System Architect
 - [Homepage](https://derrickxunu.github.io/)
 - [Linkedin](https://www.linkedin.com/in/runsheng-xu/)
 
 Xu Han (Ph.D. Student @ UCLA):
-- Map customization
+- Maps
 - [Linkedin](https://www.linkedin.com/in/xu-han-12851a64/)
 
 Hao Xiang (Ph.D. Student @ UCLA):
-- Trajectory prediction module, functionality enhancement
+- Trajectory Prediction, Functionality Enhancement
 - [Homepage](https://xhwind.github.io/)
 - [Linkedin](https://www.linkedin.com/in/hao-xiang-42bb5a1b2/)
 
-### Theoretical Support
 Dr. Xin Xia (Postdoc @ UCLA):
-- Theoretical guidance on cooperative localization
+- Cooperative Localization
 - [Linkedin](https://www.linkedin.com/in/xin-xia-3b6589189/)
 
 Dr. Yi Guo (Postdoc @ UC):
-- Help on Sumo part
+- Platooning algorithm, SUMO Only Mode (under dev)
 - [Linkedin](https://www.linkedin.com/in/yi-guo-4008baaa/)
 
-### Supervisor
-Dr. Jiaqi Ma (Associate Professor @ UCLA)
-- Project and lab supervisor
-- [Linkedin](https://www.linkedin.com/in/jiaqi-ma-17037838/)
-- [Google Scholar](https://scholar.google.com/citations?user=S3cQz1AAAAAJ&hl=en)

docs/md_files/customization.md

@@ -28,7 +28,7 @@ class LocalizationManager(object):
         ...
         corrected_cords = self.kf(x, y, z, speed, yaw, imu_yaw_rate)
 ```
-If a user wants to remain the whole structure of localization and just replace the filter(e.g. Extended Kalman Filter),
+If a user wants to remain the whole structure of localization and just replace the filter (e.g. Extended Kalman Filter),
 then he/she just needs to create a `localization_manager.py` under `opencda/customize/core/sensing/localization`
 folder and initializes the `CustomizedLocalizationManager` with Extended Kalman Filter:
 
@@ -142,7 +142,7 @@ through `update_info()`, and then takes the `target_speed` and `target_loc` gene
 behavior agent to produce the final control command through `run_step()`.
 
 Different from other modules, `ControlManager` is more like a abstract class, which provides an
-interface to call the corresponding controller(default pid controller).
+interface to call the corresponding controller (default pid controller).
 
 ```python
 import importlib