DCRNN

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This repo contains all the codes and sample files for the DCRNN paper

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# Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification

This repository contains all the implementations (including proposed models as well as baselines) and sample files for [the D-CRNN paper](https://arxiv.org/abs/2102.05843).

## Feature Engineering 
* __Statistical Feature Matrix__: to generate feature matrix as input for deep models, use ```Create_FeatureMatrix.py```. 
* __Feature Vector V1__: to generate the original feature vector for a trajectory to be used by GBDT model, use ```Create_FeatureVector_V1.py```. 
* __Feature Vector V2__: to generate the modified feature vector for a trajectory to be used by GBDT model, use ```Create_FeatureVector_V2.py```.

## Models
* __D-CRNN__: this is our proposed model to perform driver prediction based on driving style information. This model combines several important compoenents of deep-neural-network architectures including recurrent, convolutional, and fully connected components. An implementation of this model in Tensorflow can be find [here](https://github.com/sobhan-moosavi/DCRNN/blob/master/DCRNN.py). Following diagram also describes the design of this model: <center><img src="/files/D-CRNN_2.png" width="1100"></center>

* __VRAE__: this is the Variational Recurrent Auto-encoder model, proposed by [Fabius and Amersfoort (2015)](https://arxiv.org/abs/1412.6581). Here we extend the [original implementation](https://github.com/y0ast/Variational-Recurrent-Autoencoder) to use it for driver prediction task by a modified loss function. The implementation of this model in Theano can be find [here](https://github.com/sobhan-moosavi/DCRNN/blob/master/VRAE.py).

* __GBDT__: this is a Gradient Boosting Decision Tree model which we use it for driver prediction task. A Python imeplementatin of this method based on scikit-learn library is available [here](https://github.com/sobhan-moosavi/DCRNN/blob/master/GBDT.py).

* __CNN-model__: this is a Convolutional Neural Network model to perform driver prediction task, which is proposed by [Dong et al. (2016)](https://arxiv.org/abs/1607.03611). Imeplementation of this method in Tensorflow can be find [here](https://github.com/sobhan-moosavi/DCRNN/blob/master/CNN_model.py).

* __RNN-model__: this is a Recurrent Neural Network model to perform driver prediction task, which is proposed by [Dong et al. (2016)](https://arxiv.org/abs/1607.03611). Imeplementation of this method in Tensorflow can be find [here](https://github.com/sobhan-moosavi/DCRNN/blob/master/RNN_model.py).

## Requirements
* __Python__: You may use either Python 2.7 or 3. 
* __Tensorflow__: For all the deep models, except VRAE, you need Tensorflow (```version >= 1.12.0``` is recommended). 
* __Theano__: To run VRAE model, you need Theano (```version >= ???``` is recommended). 
* __Cuda Library__: You need Cuda Library for tensorflow and theano-based codes; ```version > 9.0.176``` is recommended.

Note that our models can be run on both CPU and GPU machines.

## How to Run
__Creating Feature Vector/Matrix__
* Generate Feature Matrix: Use ```python Create_FeatureMatrix.py``` to create feature matrix. This will result in creating two files in __data__ directory, one _npy_ and one _pkl_ file. 
* Generate Feature Vector: This is to generate input for non-deep models, such as GBDT. For the original version of features as described [here](), use ```Create_FeatureVector_V1.py```, and for the modified version as described in [our paper](), use ```Create_FeatureVector_V2.py```. For each data file, we create one _npy_ and one _pkl_ file, using either of scripts.

__Modeling and Prediction__
* Run Deep Models: You just need to use ```python [MODEL_NAME].py```. Make sure you have all the requirements satisfied. 
* Run GBDT Models: Set the desired input data file in script and use ```python GBDT.py``` to run the model. Use ```--version v1``` for the original feature vector (size 321), or ```--version v2``` for the modified version (size 384).

## Sample Data
You may find a raw sample file in [data](https://github.com/sobhan-moosavi/DCRNN/tree/master/data) directory. In this file we have 5 drivers, and 10 random trajectories for each driver. The format of this file is described as follows:

| Attribute | Description |
|:---------:|-------------|
|Driver| Indicates driver id, which is a string. |
|ID| Indicates trajectory id, which is a string. |
|Time| An integer which indicates the timestep for a datapoint of a trajectory. |
|Lat| Shows the latitude value of GPS coordinate. |
|Lon| Shows the longitude value of GPS coordinate. |
|Speed| Shows the ground velocity of the vehicle as reported by OBD-II port. |
|Acceleration| Shows the rate of change of ground velocity or speed. |
|RPM| Shows the round per minute, as reported by OBD-II port. |
|Heading| Shows the bearing of the vehicle, which is a value between 0 and 359. |
|AccelX| Shows the acceleration sensor reading along with X-axis. |
|AccelY| Shows the acceleration sensor reading along with Y-axis. |
|AccelZ| Shows the acceleration sensor reading along with Z-axis. |

<!--
## Sample Results
Following is the result of different models on a random sample set of 50 drivers, with 50 trajectories for each driver. For deep models, we report accuracy on both segment as well as trajectory, see the [paper](#) for details.

| Model | Accuracy--Segment | Accuracy--Trajectory |
|:-----:|:-----------------:|:--------------------:|
| GBDT-V1| -- | 25.13% |
| GBDT-V2| -- | 49.24% |
| CNN-model | 42.83% | 59.90% |
| RNN-model | 55.45% | 70.73% |
| VRAE | 51.28% | 63.67% |
| D-CRNN | __62.94%__ | __78.00%__ |
-->

## Acknowledgments 
* [Sobhan Moosavi, Pravar D. Mahajan, Srinivasan Parthasarathy, Colleen Saunders-Chukwu, and Rajiv Ramnath; _"Driving Style Representation in Convolutional Recurrent Neural Network Models of Driver Identification"_, 2020](https://arxiv.org/abs/2102.05843)

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