# CarND-Capstone

**Repository Path**: software_test/CarND-Capstone

## Basic Information

- **Project Name**: CarND-Capstone
- **Description**: 基于ROS的路径点循迹、交通灯识别。使用来自udacity的仿真器
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 3
- **Forks**: 0
- **Created**: 2020-04-13
- **Last Updated**: 2025-04-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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).

### Native Installation

* Be sure that your workstation is running Ubuntu 16.04  Ubuntu 18.04 . [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

* 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/81e63fcc335d7b64139d7482017d6a97b405e250/ROS_SETUP.md?fileviewer=file-view-default)
* Download the [Udacity Simulator](https://github.com/udacity/CarND-Capstone/releases).

### Usage

1. Clone the project repository

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

### Result  
I record the performance of my code in simulator. Please check this [video](project_output.mp4)