文件列表:

Dockerfile (1241, 2020-07-17)
LICENSE (1075, 2020-07-17)
data (0, 2020-07-17)
data\churchlot_with_cars.csv (1961, 2020-07-17)
data\grasshopper_calibration.yml (659, 2020-07-17)
data\maptf.launch (136, 2020-07-17)
data\sim_waypoints.csv (272679, 2020-07-17)
data\wp_yaw.txt (300881, 2020-07-17)
data\wp_yaw_const.csv (334641, 2020-07-17)
imgs (0, 2020-07-17)
imgs\autoware_computing.png (253134, 2020-07-17)
imgs\autoware_tf1.png (404590, 2020-07-17)
imgs\autoware_tf2.png (137945, 2020-07-17)
imgs\open_simulator.png (149817, 2020-07-17)
imgs\select_waypoint.png (561527, 2020-07-17)
imgs\unity.png (222015, 2020-07-17)
requirements.txt (189, 2020-07-17)
ros (0, 2020-07-17)
ros\.catkin_workspace (98, 2020-07-17)
ros\launch (0, 2020-07-17)
ros\launch\site.launch (1009, 2020-07-17)
ros\launch\styx.launch (815, 2020-07-17)
ros\src (0, 2020-07-17)
ros\src\CMakeLists.txt (64, 2020-07-17)
ros\src\camera_info_publisher (0, 2020-07-17)
ros\src\camera_info_publisher\CMakeLists.txt (6826, 2020-07-17)
ros\src\camera_info_publisher\launch (0, 2020-07-17)
ros\src\camera_info_publisher\launch\camera_info_publisher.launch (233, 2020-07-17)
ros\src\camera_info_publisher\package.xml (2145, 2020-07-17)
ros\src\camera_info_publisher\yaml_to_camera_info_publisher.py (2301, 2020-07-17)
ros\src\dbw_mkz_msgs (0, 2020-07-17)
ros\src\dbw_mkz_msgs\CHANGELOG.rst (1421, 2020-07-17)
ros\src\dbw_mkz_msgs\CMakeLists.txt (949, 2020-07-17)
ros\src\dbw_mkz_msgs\bmr (0, 2020-07-17)
ros\src\dbw_mkz_msgs\bmr\1.0.11-1.0.12 (0, 2020-07-17)
ros\src\dbw_mkz_msgs\bmr\1.0.11-1.0.12\BrakeReport.bmr (6236, 2020-07-17)
ros\src\dbw_mkz_msgs\bmr\1.0.11-1.0.12\BrakeReport.saved (2532, 2020-07-17)
ros\src\dbw_mkz_msgs\bmr\1.0.11-1.0.12\SteeringReport.bmr (3134, 2020-07-17)
... ...

This is the project repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car. For more information about the project, see the project introduction [here](https://classroom.udacity.com/nanodegrees/nd013/parts/6047fe34-d93c-4f50-8336-b70ef10cb4b2/modules/e1a23b06-329a-4684-a717-ad476f0d8dff/lessons/462c933d-9f24-42d3-8bdc-a08a5fc866e4/concepts/5ab4b122-83e6-436d-850f-9f4d26627fd9). I combined what Ive learned over the course of the entire Nanodegree Program to drive Carla, a real self-driving car, around the Udacity test track! I submitted my code to be run on Carla, Udacity's autonomous Lincoln MKZ, at their test site in Palo Alto, California! ### Notes to Reviewer Dejan Trajkovic (ci.a.odejan@gmail.com) - submitting as an individual Please use **one** of the two installation options, either native **or** docker installation. ### Native Installation * Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. [Ubuntu downloads can be found here](https://www.ubuntu.com/download/desktop). * If using a Virtual Machine to install Ubuntu, use the following configuration as minimum: * 2 CPU * 2 GB system memory * 25 GB of free hard drive space The Udacity provided virtual machine has ROS and Dataspeed DBW already installed, so you can skip the next two steps if you are using this. * Follow these instructions to install ROS * [ROS Kinetic](http://wiki.ros.org/kinetic/Installation/Ubuntu) if you have Ubuntu 16.04. * [ROS Indigo](http://wiki.ros.org/indigo/Installation/Ubuntu) if you have Ubuntu 14.04. * [Dataspeed DBW](https://bitbucket.org/DataspeedInc/dbw_mkz_ros) * Use this option to install the SDK on a workstation that already has ROS installed: [One Line SDK Install (binary)](https://bitbucket.org/DataspeedInc/dbw_mkz_ros/src/81e63fcc335d7b***139d7482017d6a97b405e250/ROS_SETUP.md?fileviewer=file-view-default) * Download the [Udacity Simulator](https://github.com/udacity/CarND-Capstone/releases). ### Docker Installation [Install Docker](https://docs.docker.com/engine/installation/) Build the docker container ```bash docker build . -t capstone ``` Run the docker file ```bash docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone ``` ### Port Forwarding To set up port forwarding, please refer to the [instructions from term 2](https://classroom.udacity.com/nanodegrees/nd013/parts/40f38239-66b6-46ec-ae68-03afd8a601c8/modules/0949fca6-b379-42af-a919-ee50aa304e6a/lessons/f758c44c-5e40-4e01-93b5-1a82aa4e044f/concepts/16cf4a78-4fc7-49e1-8621-3450ca938b77) ### Usage 1. Clone the project repository ```bash git clone https://github.com/udacity/CarND-Capstone.git ``` 2. Install python dependencies ```bash cd CarND-Capstone pip install -r requirements.txt ``` 3. Make and run styx ```bash cd ros catkin_make source devel/setup.sh roslaunch launch/styx.launch ``` 4. Run the simulator ### Real world testing 1. Download [training bag](https://s3-us-west-1.amazonaws.com/udacity-selfdrivingcar/traffic_light_bag_file.zip) that was recorded on the Udacity self-driving car. 2. Unzip the file ```bash unzip traffic_light_bag_file.zip ``` 3. Play the bag file ```bash rosbag play -l traffic_light_bag_file/traffic_light_training.bag ``` 4. Launch your project in site mode ```bash cd CarND-Capstone/ros roslaunch launch/site.launch ``` 5. Confirm that traffic light detection works on real life images

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