Label Now, Drive Later: Why Autonomous Driving Needs Fewer Clicks, Not Smarter Annotators
Opening — Why this matters now Autonomous driving research does not stall because of missing models. It stalls because of missing labels. Every promising perception architecture eventually collides with the same bottleneck: the slow, expensive, and error-prone process of annotating multimodal driving data. LiDAR point clouds do not label themselves. Cameras do not politely blur faces for GDPR compliance. And human annotators, despite heroic patience, remain both costly and inconsistent at scale. ...