Traffic Modelling Datasets

Traffic modelling is a hot topic in the field of autonomous cars currently. Here you will find a list of open datasets which can be used as source for building a traffic model. The list will include data captured from a variety of sources as listed below:

  • UAV/Drones
  • Traffic Camera
  • Autonomous Cars

This is not a list of datasets for learning-to-drive. This list is more focused towards dataset which provide global perspective of the traffic scenarios, rather than ego-vehicle perspective. Though few ego-vehicle datasets can be used for traffic modelling as well.

Datasets

  1. Argoverse

    • 3D tracking data: 113 segments of 15-30secs each.
    • Motion Forecasting: 324,557 segments of 5secs each (Total 320hrs)
    • Python APIs available for visualization on HD maps
    • Data not split into signalized and non-signalized intersections.

  2. Interaction

    • Contains roundabout, lane merging - which do not require Traffic Light info.
    • “DR_” recorded via drones. “TC_” are track files recorded via fixed cameras.
    • HD map has drive-able area and lane markings
    • No video or image available
    • Position, velocity, orientation, bbox dimension in csv
    • Python APIs available for visualization on HD maps

  3. In-D

    • Python script to visualize data available.
    • bbox dimension, center (x,y), velocity (x,y and lat-long), acceleration (x,y and lat-long), heading in csv.
    • xx_background.png contains image of the road. No other images/ videos.
    • Lane markings, drive-able area part of HD maps (.osm file).
    • Entire data at signalized intersection.

  4. High-D

    • Python script to visualize data available.
    • bbox dimension, center (x,y), velocity (x,y and lat-long), acceleration (x,y and lat-long), heading in csv.
    • xx_background.png contains image of the road. No other images/ videos.
    • Lane markings, drive-able area part of HD maps (.osm file).
    • Entire data at highway.

  5. NGSIM

    • Vehicle ID, local x,y and global x,y, vehicle width and length.
    • Video available for road and lane data. Video quality very bad!
    • CAD files of area available.

  6. Stanford

    • Contains data at 8 (~ * 5) scenes.
    • 100% view not available for all intersection. In-campus roads not a good representation of normal traffic scenarios. More pedestrian and bike data.
    • More info available here.

  7. NuScenes

    • 1000 scenes.
    • All location data is given with respect to the global coordinate system.
      • Global x, y, z
      • Bbox l, b, h
      • Rotation in Quaternion
      • Contains Pedestrian Data
      • Publication

  8. Apollo

    • The trajectory dataset consists of 53min training sequences and 50min testing sequences captured at 2 FPS
      • Global x, y, z positions
      • object length, width, height, heading
      • frame_id, object_id, object type
    • Data not split into signalized and non-signalized intersections.

  9. Round-D

    • Python script to visualize data available.
    • bbox dimension, center (x,y), velocity (x,y and lat-long), acceleration (x,y and lat-long), heading in csv.
    • xx_background.png contains image of the road. No other images/ videos.
    • Lane markings, drive-able area part of HD maps (.osm file).
    • Full data on roundabouts.

Below we mention several parameters crucial for learning behavior and interactions between vehicles in a recorded scenario against each dataset.

S.No. Data Source Road Lane Boundary Vehicle Pedestrian Traffic Light
1 Argoverse ✔️ ✔️ ✔️ ✔️ ❌
             
2 Interaction ✔️ ✔️ ✔️ ❌ ❌
             
3 In-D ✔️ ✔️ ✔️ ✔️ ❌
             
4 High-D ✔️ ✔️ ✔️ ✔️ ❌
             
5 NGSIM ✔️ ✔️ ✔️ ❌ ✔️
             
6 Stanford ❌ ❌ ✔️ ✔️ ❌
             
7 NuScenes ✔️ ✔️ ✔️ ✔️ ❌
             
8 Apollo ❌ ❌ ✔️ ✔️ ❌
             
9 Round-D ✔️ ✔️ ✔️ ✔️ ❌