https://github.com/maudzung/self-driving-car-04-behavior-cloning
https://github.com/maudzung/self-driving-car-04-behavior-cloning
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/maudzung/self-driving-car-04-behavior-cloning
- Owner: maudzung
- Created: 2020-05-09T05:02:18.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-12T08:26:04.000Z (over 5 years ago)
- Last Synced: 2025-01-21T15:22:08.184Z (10 months ago)
- Language: Python
- Size: 119 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **Behavioral Cloning Project**
## Demostration
The full demostration is at [https://youtu.be/0fgVSD8TWUc](https://youtu.be/0fgVSD8TWUc)
## Objectives
The 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
(I check the [rubric points](https://review.udacity.com/#!/rubrics/432/view))
---
## Source code structure
My project includes the following files:
```shell script
${ROOT}
├──data/
├──IMG/
├──driving_log.csv
├──images/ (Images for writing the note)
├──data_utils.py (containing the script to load data information and create data generators for training and validation set
├──drive.py (for driving the car in autonomous mode)
├──model.py (containing the script to create and train the model)
├──model.h5 (containing a trained convolution neural network)
├──README.md (summarizing the results)
├──video.mp4 (for demostration of the results)
├──video.py (write images (after running drive.py) to a video)
├──transformation.py (for augmentation dataset)
```
## Dataset
### Data collection
I collected data with udacity self-driving car simulator (downloaded Simulator Term 1 [ here](https://github.com/udacity/self-driving-car-sim)).
The Udacity simulator has two modes, training and autonomous, and two tracks.
Training mode is to log the data for learning the driving behaviour.
My strategy for data collection:
- Three laps of center lane driving
- Two laps of recovery driving from the sides
- One lap focusing on driving smoothly around curves
The simulator car had 3 front cameras that are the left camera, the right camera, and the center camera.
I collected and used all images from the three cameras to train and validate my model.
The setup of cameras and steering control were described in the below figure:
### Data Pre-process & Augmentation
First, I applied random horizontal flip and random brightness to the original image.
Random horizontal flip:
Random brightness:
Second, I cropped the original image to remove the head of the car and the scence that is too far from the car.
Third, I normalized images to a range of -0.5 to 0.5 (line #27 in the `model.py`)
I tried to resize images to a size of (66, 200, 3) and convert the RGB images to YUV spaces as mentioned in the papers.
However, results were not good, so I decided to not apply the resizing and converting steps.
## Model Architecture
My model bases on [Nvidia model](https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf).
It consists of 9 layers, including a normalization layer, 5 convolutional layers, and 4 fully connected layers (model.py lines 18-43).
I tried to applied Dropout layers before Fully connected layers, but results are not good.
Model architecture:
## Training Strategy
- I randomly shuffled the data set and separated the collected data into two parts: 80% for training, 20% for validation (line #67 in `model.py`).
- The model used an adam optimizer, the learning rate was set to 0.001 (model.py line 25).
- The batch size is 64.
- I trained the model with 10 epochs.
The progress of training as below:
## How to run
Using the Udacity provided simulator and my drive.py file, the car can be driven autonomously around the track by executing
```sh
python drive.py model.h5
```
Optionally, the speed of the car could be changed in line #50 in `drive.py` file. By default, the speed was set to 9 MPH.