SDAV — SmartData for Autonomous Vehicles	Management	GitLab	SeaFile	Manager

SDAV: OpenCOOD SmartData Model

Dataset

OpenCOOD is an Open COOperative Detection framework for autonomous driving. More details on the dataset can be found at OpenCOOD Github .

Dataset at LISHA's IoT

domain: opencood
username: opencood
password: A7B64D415BD3E9AA
version: 1.2 (Mobile)

Simulations

There are two simulations available with multiple vehicles. Each vehicle operates as EGO and collects frontal camera and lidar samples as described further below. Each vehicle is accompanied by a collection of vehicle ground truths. The vehicles are selected at each timestamp whenever they are hit by more than 3 lidar rays. This means that the time series of each ground truth has a different size. If a vehicle does not have a sample at a specific timestamp it could not be identified through perception.

Simulation 1:
- Vehicles:
  - signature: 641 , t0: 1629145617450000 , tf: 1629145659250000 , length= 4.901683330535889 , width= 2.128324270248413 , height= 1.5107464790344238
    - Ground Truth of Perception: Signatures: 650, 659, 669, 673, 677, 682, 684, 685, 689, 690, 691, 692, 693, 695, 696, 698, 706, 708, 710, 712, 714, 720, 727, 728, 729, 730, 732, 740, 744, 746, 747, 748, 751, 688, 679, 750, 671, 737
  - signature: 650 , t0: 1629145617450000 , tf: 1629145659250000 , length= 4.901683330535889 , width= 2.128324270248413 , height= 1.5107464790344238
    - Ground Truth of Perception: Signatures: 641, 659, 671, 677, 682, 684, 685, 687, 688, 689, 691, 692, 693, 698, 705, 708, 710, 712, 714, 720, 727, 728, 730, 732, 737, 740, 744, 746, 747, 748, 750, 751, 695, 669, 676, 738, 755, 706
  - signature: 659 , t0: 1629145617450000 , tf: 1629145659250000 , length= 4.901683330535889 , width= 2.128324270248413 , height= 1.5107464790344238
    - Ground Truth of Perception: Signatures: 641, 650, 671, 677, 685, 687, 689, 691, 692, 693, 695, 698, 705, 706, 708, 712, 714, 727, 730, 732, 737, 738, 740, 746, 747, 748, 750, 751, 755, 688, 676, 669, 733, 739, 743, 718, 682, 717, 736, 713, 683, 742, 710

Simulation 2:
- Vehicles:
  - signature: 1188 , t0: 1629306665500000 , tf: 1629306677600000 , len= 4.901683330535889 , width= 2.128324270248413 , height= 1.5107464790344238
    - Ground Truth of Perception: Signatures: 1197, 1206, 1225, 1227, 1228, 1229, 1231, 1226, 1232, 1230, 1215, 1233
  - signature: 1197 , t0: 1629306665500000 , tf: 1629306677600000 , len= 4.901683330535889 , width= 2.128324270248413 , height= 1.5107464790344238
    - Ground Truth of Perception: Signatures: 1188, 1206, 1225, 1229, 1231, 1227, 1228, 1226
  - signature: 1206 , t0: 1629306665500000 , tf: 1629306677600000 , len= 4.901683330535889 , width= 2.128324270248413 , height= 1.5107464790344238
    - Ground Truth of Perception: Signatures: 1188, 1197, 1215, 1225, 1227, 1228, 1229, 1231, 1226, 1232, 1230, 1233
  - signature: 1215 , t0: 1629306665500000 , tf: 1629306677600000 , len= 4.901683330535889 , width= 2.128324270248413 , height= 1.5107464790344238
    - Ground Truth of Perception: Signatures: 1206, 1226, 1227, 1228, 1230, 1232, 1233, 1224, 1188

Vehicle Classes

Vehicle Motion Vectors are stored with a unit containing the class of the vehicle (ETSI). The class represents is related to the dimensions of the vehicle:

Class	SmartData Unit	Dimensions (Length, Width, Height in meters)
Unknown	0x40000001	N.A.
Moped	0x40010001	2.35 x 0.88 x 1.50
Motorcycle	0x40020001	2.30 x 0.90 x 0.60
Passenger Car	0x40030001	4.42 x 1.98 x 1.55
Bus	0x40040001	10.20 x 2.46 x 3.23
Light Truck	0x40050001	6.90 x 2.90 x 3.40
Heavy Truck	0x40060001	15.00 x 2.45 x 4.00
Trailer	0x40070001	12.00 x 2.50 x 4.30
Special Vehicle	0x40080001	N.A.
Tram	0x40090001	16.00 x 3.00 x 4.00
Emergency Vehicle	0x400a0001	5.20 x 2.30 x 2.74
Agricultural	0x400b0001	4.36 x 2.01 x 2.49

The ground truth motion vectors have all been stored as Passenger Cars.

IMU Data

Period: 100ms

Name	Original Quantity	SmartData Unit	SmartData Quantity	Conversion	Semantics
IMU Acceleration	m/s²	0xC4962924 (F32)	m/s²		Accelerometers' readings of the AV, longitudinal (dev=0), lateral (dev=1), vertical (dev=2).
IMU Rotation	Degree	0xC4B24924 (F32)	rad	value * PI/180	Roll, Yaw, Pitch angle in radians (dev=0, 1, 2).
IMU Angular Velocity	rad/s	0xC4B23924 (F32)	rad/s		Gyroscopes Roll, Yaw, Pitch rate in rad/s (dev=0, 1, 2).
Compass (Heading)	rad	0xC4B24924 (F32)	rad		The heading of the speed vector of the object in relation to the true north (WGS84) in radians (dev=3)

Control Data

Period: 100ms

Name	Original Quantity	SmartData Unit	SmartData Quantity	Conversion	Semantics
Steering	Degree	0xC4B24924 (F32)	rad	value * PI/180	The angle of the steering wheel (dev=4)

LIDAR Data

Period: 100ms

Description	Unit	Desc	Semantics
Point Cloud LIDAR	0x03020001	type=3, subtype=2 (xyz,intensity), l=length of configuration (1)	Central 360 degrees lidar (dev=0)

Original format: ".pcd" with x, y, z, and color as columns (values in F32)
Convert blob returned query back to PCD: base64_decode twice.

Camera Data

Period: 100ms

Description	Unit	Desc	Semantics
Image	0x02240003	type=2, subtype=PNG (36), l=config 3, between 4MB and 20MB	front center (dev=0)

Original format PNG 4096x4096 pixels
Conversion: None, image is read as binary and sent to platform as a blob
Convert blob returned query back to PNG: base64_decode twice.

Getters

A set of getter examples in multiple different languages is available in the IoT Platform documentation - Example Scripts.
Important Note: On the retrieval of large data (digital data with size > 8 bytes e.g., camera, lidar or MV Global), a single base64 decodes is required to recover the original sample.
Note that a mobile series is represented with at least:

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"series" : Object {
    "unit" : unsigned long
    "t0" : unsigned long long
    "tf" : unsigned long long
    "signature" : unsigned int
}

Note that the Global Motion Vectors from the ground truth can be read with the following Python code: