Defining, Measuring, and Guaranteeing Quality for Autonomous Driving
Good annotation and testing practices are the foundations of building a great model. However, understanding what constitutes quality data is a tricky question.
Autonomous Transportation
Building a Robust Automotive LiDAR Annotation Quality Rubric
The purpose of a LiDAR annotation quality rubric is simple: it ensures that two people will score the same object in identical ways.
Challenges & Solutions for 3D LiDAR Annotation & 3D Data Sets
In this webinar, discover the challenges & solutions to 3D LiDAR annotation & 3D data sets for solving autonomous driving or driver assistance.
The Traffic Light Problem for Autonomous Vehicles
The traffic light problem for autonomous vehicles is critical for all vehicle safety, and unlike human-drivers, AVs rely solely on computer vision systems to navigate the world around us.
Moving Toward Level 4 Autonomous Driving
Kirk Boydston, Training Data Specialist at Sama shares five considerations to move your machine learning model toward level 4 autonomous driving.
Takeaways from AutoAI Conference 2018
Last week, Sama visited the Auto.AI event, which bills itself as the platform bringing together the stakeholders who play an active role in the deep driving, computer vision, and sensor fusion.
