Udacity Self Driving Car Nanodegree - Project #3
2017/7/25
This project uses Keras to train a convolutional neural network to do end-to-end steering angle control based on road images from a Udacity driving simulator. Training data from driving around one track was used to generate a saved model file that can be used to drive the car autonomously on both provided tracks. This demonstrates the neural network's ability to generalize road feature detection and control steering by cloning real driving behavior, similar to the results from the NVIDIA Apr 25, 2016 paper "End to End Learning for Self-Driving Cars."
Driving on Track 1 after learning:
Driving on optional Track 2 using only learning from Track 1:
For more details about the results of this activity, see the project writeup document.
| File | Description |
|---|---|
| load_data.py | Python code to gather data sets for model.py to use |
| model.py | Python code to train and save Keras model as model.h5 |
| drive.py | Python code to use model.h5 to drive Udacity simulator |
| model.h5 | Saved Keras trained model file |
| writeup_P3.md | The project writeup explaining the results |
| video.mp4 | Video capture of simulator autonomously driving Track #1 |
The original Udacity project repository is here.
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Set up Udacity Self-Driving Car Term 1 Starter Kit environment (Python 3, NumPy, Matplotlib, OpenCV, TensorFlow)
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Run the Udacity Simulator in "Fastest" graphics quality mode and select a track to drive autonomously.
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Run the following to start the autonomous driving commands:
python drive.py model.h5