V2V4Real

The first large-scale, real-world multimodal dataset

for Vehicle-to-Vehicle (V2V) perception

What does V2V4Real provide?

V2V4Real is the first large-scale real-world dataset for Vehicle-to-Vehicle (V2V) cooperative perception in autonomous driving.

It features:

  • Collected by two vehicles simultaneously in the same location, providing multi-view sensor datastream
  • 410 km of the driving area, 20K LiDAR, 40K RGB, and 240K annotated 3D bounding boxes across 5 vehicle classes.
  • Diverse road types included: Intersections, highway entrance ramps, straight highway roads, and straight city roads.
  • High-definition (HD) maps provided
  • Three cooperative perception tasks supported: 3D object detection, object tracking, and Sim2Real domain adaptation, with benchmarks using sota models.

Data Collection

LiDAR:
  • velodyne 32 channel lidar
  • 1.2M points per second, 10 Hz
  • 200m capturing range
  •   −25◦ to 15◦ vertical FOV ,  ±3 cm error
Camera:
  • 2x RGB camera with 1920 × 1080 resolution, 110◦ FOV
Localization:
  • Tesla: RT3000
  • Ford: Novatel SPAN E1
Collection route:
  • Freeway with one to five lanes
  • City road, one to two lanes
  • Highway, two to four lanes

Data Annotation

3D Bounding Boxes Annotation:
  • SusTechPoint is used to annotate 3D bounding boxes for LiDAR data.
  • Two groups of professional annotators label and refine five object classes.
  • Each object is annotated with its 7-degree-of-freedom 3D bounding box, driving state, and consistent ID and size.
  • Consistent ID and size are assigned to the same object in different timestamps.
Map Annotation:
  • HD map generation pipeline includes a global point cloud map and vector map.
  • LiDAR odometry is constructed using the transformation and GPS/IMU information.
  • Points are transformed onto the map coordinate to form a global point cloud map.
  • OpenDRIVE maps are outputted and converted to lanelet maps as the final format.

Benchmark

Cooperative 3D object detection benchmark
 

Sync

Async

AM

Method

AP@IoU=0.5

AP@IoU=0.7

AP@IoU=0.5

AP@IoU=0.7

(MB)

No Fusion

39.8

22.0

39.8

22.0

0

Late Fusion

55.0

26.7

50.2

22.4

0.003

Early Fusion

59.7

32.1

52.1

25.8

0.96

F-Cooper

60.7

31.8

53.6

26.7

0.20

V2VNet

64.5

34.3

56.4

28.5

0.20

AttFuse

64.7

33.6

57.7

27.5

0.20

V2X-ViT

64.9

36.9

55.9

29.3

0.20

CoBEVT

66.5

36.0

58.6

29.7

0.20

Cooperative Tracking benchmark

Method

AMOTA()

AMOTP()

sAMOTA()

MOTA()

MT()

ML()

No Fusion

16.08

41.60

53.84

43.46

29.41

60.18

Late Fusion

29.28

51.08

71.05

59.89

45.25

31.22

Early Fusion

26.19

48.15

67.34

60.87

40.95

32.13

F-Cooper

23.29

43.11

65.63

58.34

35.75

38.91

V2VNet

28.64

50.48

73.21

63.03

46.38

28.05

AttFuse

30.48

54.28

75.53

64.85

48.19

27.83

V2X-ViT

30.85

54.32

74.01

64.82

45.93

26.47

CoBEVT

32.12

55.61

77.65

63.75

47.29

30.32

 

Domain Adaptation benchmark

Method

AP@IoU=0.5

AP drop

AttFuse

22.5

42.2

AttFuse w/D.A.

23.4 (+0.9)

41.3

F-Cooper

23.6

37.1

F-Cooper w/D.A.

37.3 (+13.7)

23.4

V2VNet

23.2

41.3

V2VNet w/D.A.

26.3 (+3.1)

38.2

V2X-ViT

27.4

37.5

V2X-ViT w/D.A.

39.5 (+12.1)

25.4

CoBEVT

32.6

33.9

CoBEVT w/D.A.

40.2 (+7.6)

26.3

 

Download

LiDAR + Labels (OPV2V format)

train1 [US, Asia]       test 1 [US, Asia]

train2 [US, Asia]       test 2 [US, Asia]

train3 [US, Asia]       test 3 [US, Asia]

train4 [US, Asia]

train5 [US, Asia]

train6 [US, Asia]

train7 [US, Asia]

train8 [US, Asia]

LiDAR + Labels ( Kitti format):   google drive link
HDMap annotation: [next version]
Camera Data: [next version]
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