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mbechtel2 edited this page Jan 13, 2019 · 3 revisions

DeepPicar

DeepPicar is a low-cost autonomous RC car platform using a deep convolutional neural network (CNN). DeepPicar is a small scale replication of NVIDIA's real self-driving car called Dave-2, which drove on public roads using a CNN. DeepPicar uses the same CNN architecture of NVIDIA's Dave-2 and can drive itself in real-time locally on a Raspberry Pi 3.

Video:

DeepPicar Driving

Some other examples of the DeepPicar driving can be found at: https://photos.app.goo.gl/q40QFieD5iI9yXU42

If you wish to recreate the paper's findings, you can train a model using our dataset which can be found at: https://drive.google.com/open?id=1LjIcOVH7xmbxV58lx3BClRcZ2DACfSwh

Hardware Configuration

DeepPicar is comprised of the following components:

  • Raspberry Pi 3 Model B: $35
  • New Bright 1:24 scale RC car: $10
  • Playstation Eye camera: $7
  • Pololu DRV8835 motor hat: $8
  • External battery pack & misc.: $10

Please refer to Parts and Assembly for assembly steps.

Installation

Install the following:

$ sudo apt-get install python-opencv python-serial python-dev

You also need to install Tensorflow.

The repository can then be cloned with the following command:

$ git clone --depth=1 https://github.com/mbechtel2/DeepPicar-v2

Driving DeepPicar

This section gives a quick overview of controlling the DeepPicar. More in-depth steps can be found in Setup and Operation.

For manual control:

$ python picar-mini-kbd-common.py  -t <throttle %> -n <#of cpu to use>

The controls are as follows:

  • 'a': drive the car forward
  • 'z': drive the car backward
  • 's': stop the car
  • 'j': turn the car left
  • 'k': center the car
  • 'l': turn the car right
  • 't': toggle video view
  • 'r': record video

For autonomous control:

$ python  picar-mini-kbd-common.py -t <throttle %> -n <#of cpu to use> -d

Model Training

Before training a model, the following changes should be made:

Change model (file) name:

model_load_file = os.path.abspath('...') #Replace ... with a name for the model

Change if normal category is to be used:

use_normal_category = True #True = equally select center/curve images, False = no equal selection

Select epochs to be used for training and validation in the params.py file:

epochs['train'] = [...] #Replace ... with integer values used to represent epochs  
epochs['val'] = [...] #Replace ... with integer values used to represent epochs

After all of the above steps are completed, The model can then be trained by running:

$ python train.py

Embedded Computing Platform Evaluation

By default, the platforms are tested over epoch 6 (out-video-6.avi), but the epochs processed can be changed by altering epoch_ids in test-model.py:

epoch_ids = [...] #Replace ... with all epochs to be processed

Also, epochs can be processed more than once (i.e. epoch_ids = [6,6] would have the platform process epoch 6 twice).

The number of frames processed can be increased/decreased as well by changing:

NFRAMES = _ #Replace _ with the total number of frames to process

Evaluation

Setup

Before running evaluations, the following steps should be taken:

Create the directory where all test results will be stored:

$ mkdir datafiles

Turn off lightdm:

$ sudo service lightdm stop

Run the appropriate scripts for maximizing performance:

$ ./scripts/maxperf.sh #Raspberry Pi 3 Only
$ ./scripts/jetson-clocks.sh #NVIDIA TX2 Only

Evaluation Scripts

For convenience, the platforms can be fully tested by running the following scripts:

Raspberry Pi 3:

$ ./scripts/model-tests.sh #Should also work on the Intel UP Board
$ ./scripts/memguard-tests.sh
$ ./scripts/palloc-tests.sh

NVIDIA Jetson TX2:

$ ./scripts/tx2-tests.sh

All scripts will create a datafiles/Dataset-temp directory that will contain all of the experiment logs. It should be renamed before running another script or its contents may be partially, or completely, overwritten.

*For the multimodel tests done in the 'model-tests.sh' script, make sure that all 'model_load_file' values in 'params.py' are different so that the same model(s) aren't used multiple times.

Acknowledgement

The DeepPicar code utilizes MIT's DeepTesla (https://github.com/lexfridman/deeptesla), which provides a TensorFlow version of NVIDIA Dave-2's CNN.

NVIDIA Dave-2 (and its CNN) is described in the following paper. https://arxiv.org/pdf/1604.07316

Citation

The paper for DeepPicar can be found at https://arxiv.org/abs/1712.08644. It can be cited using the following BibTeX entry:

@inproceedings{bechtel2017picar,
	title = {DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car},    
	author = {Michael Garrett Bechtel and Elise McEllhiney and Minje Kim and Heechul Yun},
	booktitle = {IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)},
	year = {2018}
}
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