mmDrive

In this work we explore the feasibility of purely using mmWave radars to detect dangerous driving behaviors. We then develop a novel Fused-CNN model to detect dangerous driving instances from regular driving and classify 9 different dangerous driving actions. Through extensive experiments with 5 volunteer drivers in real driving environments, we observe that our system can distinguish dangerous driving actions with an average accuracy of 97(±2)%.

Installation:

To install use the following commands.

git clone https://github.com/arghasen10/mmdrive.git
pip install -r requirements.txt

Directory Structure

mmdrive
└── models
    └── fused_cnn.py
    └── rf.py
    └── vgg_16.py
    └── helper.py
└── acoustic_fmcw
    └── Android
    └── post_process
    └── RF_Classifier
    └── README.md
└── dataset
    └── dataset_pub.pkl
└── mmwave_demo_visualizer
    └── README.md

Description

We have provided a sample subset of our dataset in the dataset directory.

To run the Fused-CNN classifier or the other baselines check models directory.

In mmwave_demo_visualizer directory we have provided the instructions to run the demo visualizer for data collection as well as real time data visualization. This implementation is made by Texas Instruments and we have slightly modified the version to enable data collection and data annotations.

In acoustic_fmcw directory we have provided the source code for the acoustic range-doppler collection.

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Reference

To refer mmDrive framework or the dataset, please cite the following work.

BibTex Reference:

@article{sen2023mmdrive,
  title={mmDrive: mmWave Sensing for Live Monitoring and On-Device Inference of Dangerous Driving},
  author={Sen, Argha and Mandal, Avijit and Karmakar, Prasenjit and Das, Anirban and Chakraborty, Sandip},
  journal={arXiv preprint arXiv:2301.08188},
  year={2023}
}
For questions and general feedback, contact Argha Sen (arghasen10@gmail.com).