Scenario 2

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Overview

Scenario 2 emulates a Vehicle-to-Infrastructure (V2I) mmWave communication setup. The adopted testbed comprises of two units. Unit 1 primarily consists of a stationary base station equipped with an RGB camera and a mmWave phased array. The stationary unit adopts a 16-element 60GHz-band phased array and it receives the transmitted signal using an over-sampled codebook of 64 pre-defined beams. The second unit (Unit 2) is a mobile vehicle unit equipped with a mmWave transmitter and GPS receiver. The transmitter consists of a quasi-omni antenna constantly transmitting (omnidirectional) at 60 GHz band. Please refer to the detailed description of the testbed presented here.

McAllister Ave: It is a two-way street with 2 lanes, a width of 10.6 meters, and a vehicle speed limit of 25mph (40.6 km per hour. It has a three-way intersection where most of the traffic takes place. This location has vehicle traffic and also footfall and cycling traffic. Around the intersection, vehicles could be seen driving through the street or driving into or out of the two parking structures located south and north of the intersection. All that variety of traffic makes the location diverse from visual and wireless perspectives alike.

Collected Data

Overview

Number of Data Collection Units: 2 (using DeepSense Testbed #1)

Number of Data Samples: 2974

Data Modalities: RGB images, 64-dimensional received power vector, GPS locations

Average Data Capture Rate: 6.77 FPS

Sensors at Unit 1: (Stationary Receiver)

Wireless Sensor [Phased Array]: A 16-element antenna array operating in the 60 GHz frequency band and receives the transmitted signal using an over-sampled codebook of 64 pre-defined beams
Visual Sensor [Camera]: The main visual perception element in the testbed is an RGB-D camera. The camera is used to capture RGB images of 960×540 resolution at a base frame rate of 30 frames per second (fps)
Position Sensor [GPS Receiver]: A GPS-RTK receiver for capturing accurate real-time locations for the stationary unit

Sensors at Unit 2: (Mobile Transmitter)

Position Sensor [GPS Receiver]: A GPS-RTK receiver is installed on the top of the mobile unit and is used to capture accurate real-time locations at 10 frames per second (fps). The collected data comprises the Latitude and Longitude information in addition to other important data

Testbed	1
Instances	2974
Number of Units	2
Total Data Modalities	RGB images, 64-dimensional received power vector, GPS locations
Unit1
Type	Stationary
Hardware Elements	RGB camera, mmWave phased array receiver, GPS receiver
Data Modalities	RGB images, 64-dimensional received power vector, GPS locations
Unit2
Type	Mobile
Hardware Elements	mmWave omni-directional transmitter, GPS receiver
Data Modalities	GPS locations

Data Visualization

Download

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How to Access Scenario 2 Data?

Step 1. Download Scenario 2 Data

Step 2. Extract the scenario2.zip file

Scenario 2 folder consists of three sub-folders:

unit1: Includes the data captured by unit 1
unit2: Includes the data captured by unit 2
resources: Includes the scenario-specific annotated dataset, data labels and other additional information. For more details, refer the resources section below.

Scenario 2 folder also includes the “scenario 2.csv” file with the paths to all the collected data. For each coherent time, we provide the corresponding visual, wireless and GPS data.

Resources

What are the Additional Resources?

Resources consist of the following information:

visual data annotations: For the visual data, we provide the coordinates of the 2D bounding box and attributes for each frame

data labels: The labels comprises of the ground-truth beam indices computed from the mmWave received power vectors, the direction of travel (unit2), and the sequence index

additional information: Includes the scenario-specific additional data. Details of the information is provided below

Visual Data Annotations

After performing the post-processing steps presented here, we generate the annotations for the visual data. Using state-of-the-art machine learning algorithms and multiple validation steps, we achieve highly accurate annotations. In this particular scenario, we provide the coordinates of the 2D bounding box and attributes for each frame. We, also, provide the ground-truth labels for 2 object classes, “Tx”, and “Distractor”. The “Tx” refers to the transmitting vehicle in the scene and “Distractor” for any other objects, such as human, other vehicles, etc. We follow the YOLO format for the bounding-box information. In the YOLO format, each bounding box is described by the center coordinates of the box and its width and height. Each number is scaled by the dimensions of the image; therefore, they all range between 0 and 1. Instead of category names, we provide the corresponding integer categories. We follow the following assignment: (i) “Tx” as “0” , and (ii) “Distractor” as “1”.

Data Labels

The labels comprises of the ground-truth beam indices computed from the mmWave received power vectors, the direction of travel (unit2), and the sequence index.

Ground-Truth Beam: The phased array of unit 1 utilizes an over-sampled beamforming codebook of N = 64 vectors, which are designed to cover the field of view. It captures the received power by applying the beamforming codebook elements as a combiner. For each received power vector of dimension [64 x 1], the index with the maximum received power value is selected as the optimal beam index. This data is provided in the column 7 [‘unit1_beam_index’] of the scenario2.csv

Sequence Index: During the data collection process, the mobile transmitter (unit2) travelled multiple times in front of the base station (unit1). For each run, the testbed collects multiple data samples. All the data samples with the same sequence index belongs to the same run

Direction of Travel: For this scenario, during the data collection process, the mobile unit generally moves either from left-to-right or right-to-left of the base station. Here “0” represents the left-to-right movement of the transmitter and “1” represents the “right-to-left” movement. The movement is measured is from the point of view of the base station. This is provided in the ‘unit2_direction’ column of the csv

Additional Information

We, further, provide additional information for each sample present in the scenario dataset. The details are provided in the columns 8 – 16 of the scenario2.csv. The contents of the additional data is listed below:

index: It represents the sample number
time_stamp[UTC]: This represents the time of data capture in “hr-mins-secs-ms” format
unit2_num_sat: For each data sample, it is an integer value representing the number of connected satellites at that time instant
unit2_sat_used: At each time instant, these were the satellites that the receiver was connected
unit2_fix_type: This shows whether or not there was a 3D fix. A 3D (three dimensional) position fix includes horizontal coordinates plus altitude. It requires a minimum of four visible satellites
unit2_DGPS: Binary indicator representing whether or not there was Differential GPS was used
unit2_PDOP: PDOP (position dilution of precision) describes the error caused by the relative position of the GPS satellites
unit2_HDOP: HDOP represents the horizontal dilution of precision

An example table comprising of the data labels and the additional information is shown below.

index	unit1_beam_index	seq_index	time_stamp[UTC]	unit2_direction	unit2_num_sat	unit2_sat_used	unit2_fix_type	unit2_DGPS	unit2_PDOP	unit2_HDOP
1	62	1	['02-11-07-0']	1	12	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21	3D	Yes	1.4	0.7
2	59	1	['02-11-08-0']	1	21	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31 B14 B27 B28 B30	3D	Yes	1.4	0.7
3	60	1	['02-11-08-166']	1	17	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31	3D	Yes	1.4	0.7
4	60	1	['02-11-08-332']	1	17	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31	3D	Yes	1.4	0.7
5	60	1	['02-11-08-498']	1	17	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31	3D	Yes	1.7	0.8
6	59	1	['02-11-08-664']	1	17	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31	3D	Yes	1.4	0.7
7	57	1	['02-11-08-830']	1	17	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31	3D	Yes	1.4	0.7
8	55	1	['02-11-09-0']	1	17	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31	3D	Yes	1.7	0.8
9	57	1	['02-11-09-142']	1	17	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31	3D	Yes	1.7	0.8
10	59	1	['02-11-09-284']	1	17	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31	3D	Yes	1.4	0.7
11	55	1	['02-11-09-426']	1	21	G2 G5 G6 G12 G19 G25 G29 R4 R5 R10 R20 R21 E11 E12 E24 E25 E31 B14 B27 B28 B30	3D	Yes	1.4	0.7

Scenario 2

License

Overview

Collected Data

Overview

Data Visualization

Download

Please login to download the DeepSense datasets

How to Access Scenario 2 Data?

Scenario 2 folder also includes the “scenario 2.csv” file with the paths to all the collected data. For each coherent time, we provide the corresponding visual, wireless and GPS data.

Resources

What are the Additional Resources?

Visual Data Annotations

Data Labels

Additional Information

An example table comprising of the data labels and the additional information is shown below.

DeepSense Scenarios

Machine Learning Tasks

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