Introducing Argoverse: data and HD maps for computer vision and machine learning research to advance self-driving technology

Argo AI staff and interns from Georgia Institute of Technology and Carnegie Mellon University, who are three of eleven co-authors on the CVPR 2019 research paper, Argoverse: 3D Tracking and Forecasting with Rich Maps. From left: Jagjeet Singh, Ming-Fang (Allie) Chang, Patsorn Sangkloy.
  • Argoverse 3D tracking dataset. A core challenge for self-driving vehicles is knowing and understanding how other objects are moving in a surrounding scene. We call this task, “3D tracking.” Our 3D tracking dataset contains several types of sensor data: 30 frames per second (fps) video from seven cameras with a combined 360-degree field of view, forward-facing stereo imagery, 3D point clouds from long range LiDAR, and a 6-degree-of-freedom pose for the autonomous vehicle. We collect this sensor data for 113 scenes that vary in length from 15 to 30 seconds. For each scene, we annotate objects with 3D bounding cuboids. In total, the dataset contains more than 10,000 tracked objects.
  • Argoverse motion forecasting dataset. For self-driving cars, it’s important to understand not just where objects have moved, which is the task of 3D tracking, but also where objects will move in the future. Like human drivers, self-driving cars need to assess, “Will that car merge into my lane?” and “Is this driver trying to turn left in front of me?” To build our motion forecasting dataset, we mined for interesting scenarios from more than 1,000 hours of fleet driving logs. “Interesting” means a vehicle is managing an intersection, slowing for a merging vehicle, accelerating after a turn, stopping for a pedestrian on the road, and more scenarios along these lines. We found more than 300,000 such scenarios. Each scenario contains the 2D, birds-eye-view centroid of each tracked object sampled at 10-hertz for five seconds. Each sequence has one interesting trajectory that is the focus of our forecasting benchmark. The challenge for an algorithm is to observe the first two seconds of the scenario and then predict the trajectory of a particular vehicle of interest for the next three seconds.
  • Argoverse high-definition maps. Perhaps the most compelling aspect of Argoverse is our high-definition mapset containing 290 kilometers of mapped lanes. The maps contain not only the location of lanes, but also how they are connected for traffic flow. So when a lane enters an intersection, the map tells you which three successor lanes a driver might follow out of that intersection. The map has two other components as well: ground height and driveable area segmentation at 1m² resolution. Taken together, these maps make many perception tasks easier, including discarding uninteresting LiDAR returns using the ground height and driveable area features. It’s easier to forecast future driving trajectories by first inferring the lane that a driver is following. Vehicle orientation and velocity estimates can be refined by considering lane attributes like direction. No doubt countless other clever ways exist to incorporate these rich maps into self-driving perception tasks, but the academic community has not yet been able to explore this combination since no previous dataset has offered high-definition maps.

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https://www.argo.ai/

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Argo AI

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https://www.argo.ai/

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