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.
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)
Sensors at Unit 2: (Mobile Transmitter)
|Number of Units||2|
|Total Data Modalities||RGB images, 64-dimensional received power vector, GPS locations|
|Hardware Elements||RGB camera, mmWave phased array receiver, GPS receiver|
|Data Modalities||RGB images, 64-dimensional received power vector, GPS locations|
|Hardware Elements||mmWave omni-directional transmitter, GPS receiver|
|Data Modalities||GPS locations|
Step 1. Download Scenario 2 Data
Step 2. Extract the scenario2.zip file
Scenario 2 folder consists of three sub-folders:
Resources consist of the following information:
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”.
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.
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:
|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|