This project is based on Course MIT 6.S094: Deep Learning for Self-Driving Cars and published on this Github The Udacity MLND template is published here Github
The goal is to predict the steering wheel angel from Tesla dataset based on the video of the forward roadway.
python 3 + Keras 2.0.1 + Tensorflow 1.0.1 + Jupyter Notebook + cv2
These models are trained by GPU Intensive workloads with 61G memory and 12G GPU memory on floydhub.com.
The final modle is trained ~10 minutes.
Databases with real-traffic video data captured and extracted 10 video clips of highway driving from Tesla:
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The wheel value was extracted from the in-vehicle CAN
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A window from each video frame is cropped/extracted and provide a CSV linking the window to a wheel value.
A snapshot of video frame:
| ts_micro | frame_index | wheel |
|---|---|---|
| 1464305394391807 | 0 | -0.5 |
| 1464305394425141 | 1 | -0.5 |
| 1464305394458474 | 2 | -0.5 |
in which, ts_micro is time stamp，frame_index denotes frame number，wheel is steering wheel angle(Based on horizontal, + is clockwise, - is anticlockwise)
The generated vedio looks like:
