Simulating LIDAR Point Cloud for Autonomous Driving using Real-world Scenes and Traffic Flows

High-quality data is crucial to building safe and effective AV systems. While many AV companies are turning to complementary techniques and simulated data to continue their work, there is often no substitute for high-quality data that captures the complex and often messy reality of driving in the real world. That’s why today we’re launching PandaSet: a new open-source dataset for training machine learning (ML) models for autonomous driving released in partnership with the LiDAR manufacturer Hesai. But as we realize more ways these technologies could improve our lives, the essential data collection and testing that power them have rightly been suspended to ensure the safety of those involved. But the pandemic has also exacerbated some of the difficulties of developing new technologies at scale.įor example, as we shelter in place around the world, the promise of autonomous vehicles (AVs) to improve access to critical goods and services has never felt more relevant.

In these unprecedented times, COVID-19 has brought out a renewed and inspiring sense of collaboration in AI and research communities as we work toward solving pressing issues.

Simulating LIDAR Point Cloud for Autonomous Driving using Real-world Scenes and Traffic Flows