This section describes the different data types and formats each sensor model uses when saving data. Refer to the page on how to capture sensor data for instructions on saving sensor model output data.

Lidar point cloud data

  • File format: .bin

Lidar point cloud data is saved in a 1-D array as a .bin file. Each point in the point cloud consists of its coordinate in 3D space (x, y, and z coordinates), and intensity as float values. The origin is set to the point where the lidar is mounted.

For Python users, the point cloud can be loaded by using the NumPy np.fromfile function.

Files are saved in SaveFile/SensorData/LIDAR_{id}. File names are automatically generated as the time and date at which the point cloud data was saved.

Semantic segmentation

Setting the Intensity Type option to semantic labels the point intensities with different color values depending on the type of object. This feature is used to create pre-labeled lidar pcd datasets. By default, semantic segmentation values follow the classification table below.

Class

Intensity (unsigned int)

Asphalt

127

Building

153

Traffic Light

190

White Lane

255

Yellow Lane

170

Blue Lane

144

Road Sign

127

Traffic Sign

132

Crosswalk

136

Stop Line

85

Sidewalk

129

Road Edge

178

Standing OBJ

109

Object On Road

92

Vehicle

86

Pedestrian

118

Obstacle

164

StopLinePrefabs

92

Light

94

Obstacle1

67

Obstacle2

101

Obstacle3

101

Obstacle4

67

Obstacle5

101

Sedan

125

SUV

135

Truck

145

Bus

155

Van

165

Stroller

40

Stroller_person

50

ElectronicScooter

60

ElectronicScooter_Person

70

Bicycle

80

Bicycle_Person

90

Motorbike

100

Motorbike_Person

110

Sportbike

120

Sportbike_Person

130

Instance segmentation

Instance segmentation works in the same method as semantic segmentation, but labels every individual object in the scene by each instance instead of by type. Intensity values are assigned based on the table below.

Class

Intensity (unsigned int)

Vehicle

0 ~ 149

Pedestrian

150 ~ 254.

Obstacle

Random (50, 100, 150, 200, 250, 45, 90, …)

3D bounding box

From version 22.R2.0, all 3D rotation angles of 3D Bounding Box are provided to improve the cognitive performance of AV (Autonomous Vehicle). Roll, Pitch and Yaw are added to 3D Bounding Box file (.txt) except Yaw.

  • File format: .txt

3D bounding boxes (or bbox for short) are boxes in three-dimensional space that delineate the region in which an object is located. 3D bbox data is saved in a simple text file following the following specification.

  • 0: class name of 3d bbox

    • Vehicle, Pedestrian, Object

  • 1 : class id of 3d bbox

    • Vehicle : 0

    • Pedestrian : 1

    • Object : 2

  • 2-4 : center_x, center_y, center_z of 3d bbox (unit : m)

    • The origin of the object coordinate system calculated based on the LiDAR coordinate system.

    • The LiDAR coordinate system follows ISO 8855 convention, which is x-axis : forward, y-axis : left, z-axis : up.

  • 5-7 : roll (x-axis), pitch (y-axis), yaw (z-axis) of 3D BBox (unit : radian)

    • The rotation angle of the object coordinate system calculated based on the LiDAR coordinate system.

  • 8-10: size_x (x-axis), size_y (y-axis), size_z (z-axis) (unit : m)

  • 11-12 : Relative distance and relative speed (unit: m, m/s)

  • 13: Unique ID of the detected object

Files are saved in SaveFile/SensorData/LIDAR_{id}. File names are automatically generated as the time and date at which the point cloud data was saved.

RGB camera image

  • File format: .jpeg

To save as RGB format, after camera installation, select ‘None’ from Ground Truth field from Image View section in Camera Setting panel.

File is saved in SaveFile/SensorData/CAMERA_*. The file name is automatically generated based on point time and date at which point cloud is saved.

Semantic segmentation image

  • File format: .png

To save as semantic label images, after camera installation, select ‘Semantic’ from Ground Truth field from Image View section in Camera Setting panel.

RBG values of segmentation image labelling map are as follows.

Class

R

G

B

Sky

0

255

255

ETC

85

22

42

Asphalt

127

127

127

Building

153

255

51

Traffic Light

255

74

240

White Lane

255

255

255

Yellow Lane

255

255

0

Blue Lane

0

178

255

Road Sign

204

127

51

Traffic Sign

99

48

250

Crosswalk

76

255

76

Stop Line

255

0

0

Sidewalk

255

102

30

Road Edge

178

178

178

Standing OBJ

113

178

37

Object On Road

178

9

90

Vehicle

255

2

2

Pedestrian

98

2

255

Obstacle

236

255

2

StopLinePrefabs

255

22

0

Light

255

2

25

Obstacle1

100

100

2

Obstacle2

200

100

2

Obstacle3

100

200

2

Obstacle4

2

100

100

Obstacle5

2

200

100

Sedan

255

60

60

SUV

255

75

75

Truck

255

90

90

Bus

255

105

105

Van

255

120

120

Stroller

120

0

0

Stroller_person

120

15

15

ElectronicScooter

140

20

20

ElectronicScooter_Person

140

35

35

Bicycle

160

40

40

Bicycle_Person

160

55

55

Motorbike

180

60

60

Motorbike_Person

180

75

75

Sportbike

200

80

80

Sportbike_Person

200

95

95

Files are named and saved in similar fashion as those for naming and saving RGB images.

Instance image

  • Image file format: .png

  • 2D bounding box: .txt

To save instance label images and 2d bbox, after camera installation, select ‘Instance’ from Ground Truth field from Image View section in Camera Setting panel.

Instance label images are labelled with different pixel values even if they are of the same class, and thus, are differentiated with ID within each class.

2d bbox in txt file follows a similar format as the KITTI data set.

  • 1 : 2d bbox classes

    • Vehicle

    • Pedestrian

    • Object

  • 2 : Truncated(1), output equal to 0.

  • 3 : Occluded(1), output equal to 0.

  • 4 : Alpha(1), output equal to 0.

  • 5-8 : Coordinates of upper left-hand, lower right-hand corners of 2d bbox. x1, y1, x2, y2, respectively.

  • 9-11 : Object dimensions. Output equals to 0 since this field overlaps with point cloud 3d bbox

  • 12-14 : Object location. Output equals to 0 since this field overlaps with point cloud 3d bbox

  • 15 : Yaw angle of object. Output equals to 0 since this field overlaps with point cloud 3d bbox

  • 16 : Relative distance between object and camera

  • 17-19 : Relative velocity of object and camera x, y, z

Files are named and saved in similar fashion as those for naming and saving RGB images.

GPS data

  • File format : txt file

GPS data format inside txt file

  • 1 : Latitude (unit : deg)

  • 2 : Longitude (unit : deg)

  • 3 : Altitude (unit : m)

  • 4 : EastOffset (unit : m)

  • 5 : NorthOffset (unit : m)

Files are saved in folder SaveFile/SensorData/GPS_*. Files are named as time and date at which the files are saved.

IMU data

  • File format : txt file

IMU data format in txt file

  • 1-2 : TimeStamp. 1 in 1-second units, 2 in 1-nanosecond units.

  • 3-6 : Orientation X, Y, Z, W. Quaternion angular velocity.

  • 7-9 : Angular Velocity X, Y, Z for X, Y, Z axis components (unit : rad/s)

  • 10-12 : Linear Acceleration X, Y, Z for X, Y, Z axis components (unit : m/s^2)

Files are saved in SaveFile/SensorData/IMU_*. Files are named as time and date at which the files are saved.

Radar Data

  • File format: bin file

Radar data, composed of position, velocity, and acceleration with respect to x, y, z axes components are saved as 4-byte data in a 1-D array format in the bin file. The origin is defined as the location at which the Radar is mounted.

  • 1-3 : Position of cluster in x, y, z

  • 4-6 : Velocity components of cluster in x, y, z

  • 7-9 : Acceleration components of cluster in x, y, z

  • 10-12 : Size of cluster in x, y, z

  • 13 : Amplitude

For python users, data can be loaded using fromfile function of numpy as follows.

Files are saved in SaveFile/SensorData/RADAR_*. Saved file names are generated based on time and date at which point cloud is saved.