Overview

Table of Contents

Overview

The Argoverse 2 motion forecasting dataset consists of 250,000 scenarios, collected from 6 cities spanning multiple seasons.

Each scenario is specifically designed to maximize interactions relevant to the ego-vehicle. This naturally results in the inclusion of actor-dense scenes featuring a range of vehicle and non-vehicle actor types. At the time of release, AV2 provides the largest object taxonomy, in addition to the broadest mapped area of any motion forecasting dataset released so far.

Download

The latest version of the AV2 motion forecasting dataset can be downloaded from the Argoverse website.

Scenarios and Tracks

Each scenario is 11s long and consists of a collection of actor histories, which are represented as “tracks”. For each scenario, we provide the following high-level attributes:

  • scenario_id: Unique ID associated with this scenario.
  • timestamps_ns: All timestamps associated with this scenario.
  • tracks: All tracks associated with this scenario.
  • focal_track_id: The track ID associated with the focal agent of the scenario.
  • city_name: The name of the city associated with this scenario.

Each track is further associated with the following attributes:

  • track_id: Unique ID associated with this track
  • object_states: States for each timestep where the track object had a valid observation.
  • object_type: Inferred type for the track object.
  • category: Assigned category for track - used as an indicator for prediction requirements and data quality.

Track object states bundle all information associated with a particular actor at a fixed point in time:

  • observed: Boolean indicating if this object state falls in the observed segment of the scenario.
  • timestep: Time step corresponding to this object state [0, num_scenario_timesteps).
  • position: (x, y) Coordinates of center of object bounding box.
  • heading: Heading associated with object bounding box (in radians, defined w.r.t the map coordinate frame).
  • velocity: (x, y) Instantaneous velocity associated with the object (in m/s).

Each track is assigned one of the following labels, which dictate scoring behavior in the Argoverse challenges:

  • TRACK_FRAGMENT: Lower quality track that may only contain a few timestamps of observations.
  • UNSCORED_TRACK: Unscored track used for contextual input.
  • SCORED_TRACK: High-quality tracks relevant to the AV - scored in the multi-agent prediction challenge.
  • FOCAL_TRACK: The primary track of interest in a given scenario - scored in the single-agent prediction challenge.

Each track is also assigned one of the following labels, as part of the 10-class object taxonomy:

  • Dynamic
    • VEHICLE
    • PEDESTRIAN
    • MOTORCYCLIST
    • CYCLIST
    • BUS
  • Static
    • STATIC
    • BACKGROUND
    • CONSTRUCTION
    • RIDERLESS_BICYCLE
  • UNKNOWN

For more additional details regarding the data schema, please refer here.

Visualization

Motion forecasting scenarios can be visualized using the viz script or by calling the viz library directly.