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https://github.com/Xiangyu-Gao/Raw_ADC_radar_dataset_for_automotive_object_detection

A dataset for the raw ADC data of 2TX-4RX MMWave Radar for automotive object detection.
https://github.com/Xiangyu-Gao/Raw_ADC_radar_dataset_for_automotive_object_detection

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A dataset for the raw ADC data of 2TX-4RX MMWave Radar for automotive object detection.

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# Raw Radar ADC Dataset for Automotive Object Detection



A dataset for the 2TX-4RX MMWave Radar with the raw ADC data being recorded. Six main objects - pedestrian, cyclist, car, motorbike, bus, truck - were collected to fit the automotive object detection scenario. 



  



## Citations



> [**RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition**](https://arxiv.org/pdf/2011.08981.pdf),            

> Xiangyu Gao, Guanbin Xing, Sumit Roy, and Hui Liu, 


> *arXiv technical report* ([arXiv 2011.08981](https://arxiv.org/abs/2011.08981))  



    @ARTICLE{9249018,  author={Gao, Xiangyu and Xing, Guanbin and Roy, Sumit and Liu, Hui},  

        journal={IEEE Sensors Journal},   

        title={RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition},   

        year={2021},  volume={21},  number={4},  pages={5119-5132},  doi={10.1109/JSEN.2020.3036047}}



> [**Raw ADC Data of 77GHz MMWave radar for Automotive Object Detection**](https://ieee-dataport.org/documents/raw-adc-data-77ghz-mmwave-radar-automotive-object-detection),            

> Xiangyu Gao, Youchen Luo, Guanbin Xing, Sumit Roy, Hui Liu, 


> *IEEE Dataport*



    @data{xm40-jx59-22, doi = {10.21227/xm40-jx59}, url = {https://dx.doi.org/10.21227/xm40-jx59},

        author = {Gao, Xiangyu and Luo, Youchen and Xing, Guanbin and Roy, Sumit and Liu, Hui},

        publisher = {IEEE Dataport},

        title = {Raw ADC Data of 77GHz MMWave radar for Automotive Object Detection},

        year = {2022} }

    

    



## Update



***(April 28, 2023) Update the description for labels.***



***(Dec. 11, 2022) Initial release of dataset and tools.***



## Introduction

In this dataset, we provided the raw **analog-to-digital-converter** (ADC) data of a **77GHz mmwave** radar for the automotive object detection scenario. The overall dataset contains approximately **19800** frames of radar data as well as synchronized camera images and labels. For each radar frame, its raw data has **_4 dimensions: samples (fast time), chirps (slow time), transmitters, and receivers_**. The experiment radar was assembled from the **TI AWR 1843** board, with 2 horizontal transmit antennas and 4 receive antennas. With **time-division multiplexing** on all transmitters, it can form a 1D-MIMO virtual array with 8 elements. 



The data collection was done on the **campus, road, and parking lot** during the daytime, with the focus of capturing the data for **_six main objects: pedestrian, cyclist, car, motorbike, bus, truck_**. The collected objects can be either **moving (mostly)** or **static**. A single data collection run consisted of multiple objects listed above moving or being static at a normal speed for 30 seconds in front of the testbed. More information in terms of dataset structure, format, tools, and radar configuration was described below.



## Download



Download the dataset from the google drive link:

```

https://drive.google.com/file/d/1QgjwdQpY96NAVGdvjjFrXLhb48o15EO_/view?usp=share_link

``` 

Or from IEEE Dataport:

```

https://ieee-dataport.org/documents/raw-adc-data-77ghz-mmwave-radar-automotive-object-detection

```



## Dataset Structure and Format



The dataset consists of multiple sequences, e.g., "2019_04_09_bms1000", "2019_04_09_cms1000". Under each sequence folder, there exists the image folder *"images_0"*, and radar data folder *"radar_raw_frame"*, and label folder *"text_labels"*.



The overall dataset structure is presented as below.



    Automotive

    ---2019_04_09_bms1000

       ---images_0

       ---radar_raw_frame

       ---text_labels

    ---2019_04_09_cms1000

       ......

       

The "radar_raw_frame" folder contains **raw ADC radar data in _.mat_ format**, and "images_0" folder contains **camera images in _.jpg_ format**, and the "text_labels" contains **label files for each frame in _.csv_ format**. The detailed data format is explained below.



**You should match the radar frame, camera image, and labels based on their filenames.** There is some redundant data and you can simply disregard it.



### Radar ADC Data



*  For each radar frame, its raw data (*.mat) has **4 dimension**: 

        

        samples (128), chirps (255), receivers (4), transmitters (2). 



    All transmitters were arranged with *time-division multiplexing* (TDM), i.e., send chirp signals one by one. The example frame structure is shown below:

  



* The placement of 2 transmitters and 4 receivers was plotted in the left figure below, from the [TI documentation](https://www.ti.com/lit/an/swra554a/swra554a.pdf). Through TDM, it forms a 1 by 8 MIMO array as shown in the right figure below:

  



* All radar configurations are included in [config](config.py).



### Camera Image



*  The camera image for each frame is 1440x1080 pixels.



### Labels



*  Each *.csv file include the labels for a frame, with **each row of it in format of _[uid, class, px, py, wid, len]_**, they are,  

        

        uid: the unique tracking id of objects in this sequence

        class: the class id of objects, with the id number represented in label map, 

        px: x-axis center of bounding box in meters within the range of [-20m, 20m]

        py: y-axis center of bounding box in meters within the range of [1m, 24m]

        wid: width of bounding box in meters (corresponding to x-axis)

        len: lengt of bounding box in meters (corresponding to y-axis)

        

        The mapping of class id to objects is:

        label_map = {0: 'person',

                     2: 'car',

                     3: 'motorbike',

                     5: 'bus',

                     7: 'truck',

                     80: 'cyclist',

                     }



Note that there might be a few special cases where the px, py values exceed the provided limit and you may just wanna ignore them or do the clipping.

## Dataset Tools



### Software Requirement and Installation



Python 3.6 (please refer to [INSTALL](requirements.txt) to set up libraries.)



Under prepare...



## License



This tool is released under MIT license (see [LICENSE](LICENSE)).



## Acknowledgement

This project was supported by the [FUNLAB](https://depts.washington.edu/funlab/), [University of Washington](http://www.washington.edu/), and [Silkwave Holdings](https://silkwave.com.hk/). This project is not possible without multiple great open-source codebases. We list some notable examples below.



* [microDoppler](https://github.com/Xiangyu-Gao/mmWave-radar-signal-processing-and-microDoppler-classification)

* [ramp-cnn](https://github.com/Xiangyu-Gao/Radar-multiple-perspective-object-detection)