Skip to content

Latest commit

 

History

History
42 lines (30 loc) · 1.33 KB

README.md

File metadata and controls

42 lines (30 loc) · 1.33 KB

Intro

End to End Learning for Self-Driving Cars

This project is a self driving car made in simulator using convolutional neural networks(CNNs) to directly map raw pixels from a three camera facing infront of the car with different field of view to steering commands.

Key Features

  • Implementation of CNN Architecture for end-to-end learning in self driving cars.
  • Augementation of training data with with artificial shifts and various transformations.

Required

  • Download simulator car from udacity github repository named "self-driving-car-sim" according to your OS. Read readme.md file for installation process.
  • make your own virtual environment
pip3 install -r requirements.txt

Running

Open the simulator car

python3 drive.py

Run the car in autonomous mode

Results of different model

There are different model which I had trained on different ways. Here are some of them and its results are also discussed below:

  • model1

    • trained on augmented image, and 4 layers of dropout
    • works good for validation data, almost like overfitting

  • model2

    • trained on augmented image, but no dropout layers
    • works very good for validation data
    • not much good for training data

  • model3

    • trained on augmented image, no dropout layers
    • but very low learning rate=0.0001 with Adam