Moving autonomous vehicles forward, together.

Open sourcing Level 5’s autonomous driving data

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Autonomous vehicles have the potential to redefine transportation. When fully realized, this technology promises to unlock a myriad of societal, environmental, and economic benefits.

In July 2019, we’re sharing a comprehensive, large-scale dataset featuring the raw sensor camera and lidar inputs as perceived by a fleet of multiple, high-end, autonomous vehicles in a bounded geographic area. This dataset will also include high-quality, human-labelled 3D bounding boxes of traffic agents, an underlying HD spatial semantic map, and a large collection of crowd-sourced imagery collected by camera-equipped ride-sharing vehicles.

With this, we aim to empower the community, stimulate further development, and share our insights into future opportunities from the perspective of an advanced, industrial autonomous vehicles program.

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Upcoming Tutorials and Competitions

CVPR 2019

Join us at CVPR as we conduct a tutorial covering the practical tips for building a Perception & Prediction system for autonomous driving. The tutorial will strike a balance between Applied Research and Engineering.

Monday, June 17th 1:30 - 4:30 PM PT, Room 104C

We’ll review the challenges involved in building a system that needs to operate without a human driver and how to push state-of-the-art neural network models into production. Audience members will learn about different kinds of labeled data needed for Perception & Prediction, and how to combine classical robotics and computer vision methods with modern deep learning approaches for Perception & Prediction.

The performance of the real-time perception, prediction and planning systems can be improved by prior knowledge of the environment, traffic patterns, expected anomalies etc. We show how a large scale fleet of camera-phone equipped vehicles can help generate those priors and help discover infrequent events increasing overall prediction performance. Finally we will walk the audience through a set of hands-on sessions into building basic blocks of self-driving stack, its challenges and how to use the presented dataset for its development & evaluation.

Presenters

  • Luc Vincent, EVP of Autonomous Technology
  • Peter Ondruska, Director of Engineering
  • Ashesh Jain, Head of Perception
  • Sammy Omari, Head of Prediction & Planning
  • Vinay Shet, Director of Product Management

Course outline

  • Introduction
  • Perception for autonomous driving
  • Prediction for autonomous driving
  • Large scale data collection for autonomous driving
  • Dataset launch and description
  • Hands-on perception and prediction session on dataset
  • Conclusion

NeurIPS 2019

This fall, we’ll facilitate a competition on 3D object detection over semantic maps. Stay tuned as more competition details are on the way!

Explore Dataset Samples

3D data annotation by Scale

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