https://github.com/nimarb/torcs-autonomous-driving

https://github.com/nimarb/torcs-autonomous-driving

dnn nengo ros ros-kinetic spiking-neural-networks torcs

Last synced: 10 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/nimarb/torcs-autonomous-driving
• Owner: nimarb
• Created: 2017-11-09T18:37:40.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-02-07T15:28:14.000Z (over 8 years ago)
• Last Synced: 2026-05-25T07:33:58.040Z (about 1 month ago)
• Topics: dnn, nengo, ros, ros-kinetic, spiking-neural-networks, torcs
• Language: TeX
• Size: 13 MB
• Stars: 3
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# TORCS autonomous driving

This repository contains source code to train and drive a car in TORCS by itself.

## Dependencies

[TORCS-ROS](https://github.com/fmirus/torcs_ros), [TORCS](https://github.com/fmirus/torcs-1.3.7), [ROS Kinetic](http://wiki.ros.org/kinetic/Installation)  

python2: nengo, keras, tensorflow, numpy, opencv    

python3: keras, tensorflow, numpy, opencv  

## How to run

 1. start torcs: `torcs`

 2. configure the desired track, choose `scr_server` as driver

 3. run: `roslaunch torcs_ros_bringup torcs_ros.launch rviz:=false driver:=false`

 4. go into the nengo_controller folder and run: `python2 controller.py`

## Structure 

The `nengo_controller` folder contains the code needed to drive the car based on all the given sensor values.

The folder `src/collect_img_sensor_data` contains a ROS node to collect training data for the DNN.

The folder `src/train-deep-neural-network` contians code to train a deep neural network to infer angle and car displacement from a driver's view input image.

    ├── final-presentation-complete

    │   └── Bilder

    ├── nengo_controller

    │   ├── data

    │   │   └── processed_data

    │   └── nengo_ros

    ├── report

    │   ├── attachments

    │   └── paper

    └── src

        ├── collect_img_sensor_data

        │   ├── data-aalborg-2laps-640x480

        │   ├── data-alpine_1-2laps-640x480

        │   ├── data-alpine_2-2laps-640x480

        │   ├── data-brondehach-2laps-640x480

        │   ├── data-cg_speedway_1-2laps-640x480

        │   ├── data-cg_track_2-2laps-640x480

        │   ├── data-cg_track_3-2laps-640x480

        │   ├── data-cg_track_3-2laps-640x480-1sthood

        │   ├── data-cg_track_3-2laps-640x480-3rdclose

        │   ├── data-cg_track_3-2laps-640x480-3rdfar

        │   ├── data-corkscrew-2laps-640x480

        │   ├── data-e_road-2laps-640x480

        │   ├── data-etrack_1-2laps-640x480

        │   ├── data-etrack_2-2laps-640x480

        │   ├── data-etrack_3-2laps-640x480

        │   ├── data-etrack_4-2laps-640x480

        │   ├── data-etrack_6-2laps-640x480

        │   ├── data-forza-2laps-640x480

        │   ├── data-olethros_road_1-2laps-640x480

        │   ├── data-ruudskogen-2laps-640x480

        │   ├── data-street_1-2laps-640x480

        │   ├── data-wheel_1-2laps-640x480

        │   ├── data-wheel_2-2laps-640x480

        │   ├── launch

        │   └── src

        └── train-deep-neural-network