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Self-Driving Car Engineer Nanodegree

Behavioral Cloning Project

The goals / steps of this project are the following:

  • Use the simulator to collect data of good driving behavior
  • Build, a convolution neural network in Keras that predicts steering angles from images
  • Train and validate the model with a training and validation set
  • Test that the model successfully drives around track one without leaving the road
  • Summarize the results with a written report

1. An appropriate model architecture has been employed

My architecture is using Keras Sequential() method and base on NVIDIA's End to End Learning for Self-Driving Cars paper.
Reference: https://arxiv.org/pdf/1604.07316v1.pdf

2. Attempts to reduce overfitting in the model

The model contains dropout layers in order to reduce overfitting.
The model was trained and validated on different data sets to ensure that the model was not overfitting.
The model was using 6th epochs.

3. Model parameter tuning

The model used an adam optimizer, so the learning rate was not tuned manually.
The model used Dropout rate was 0.5.
The model used epochs rate was 6.

4. Appropriate training data

Training data was chosen to keep the vehicle driving on the road. I used a combination of center lane driving, recovering from the left and right sides of the road.
I am not a good driver. I try my best to keep the car on the center of road.
I totally use 1694*3 record to train.