Label Efficient Visual Abstractions for Autonomous Driving

Explore a keynote presentation on label-efficient visual abstractions for autonomous driving. Delve into the trade-offs between annotation costs and driving performance in semantic segmentation-based approaches. Learn about practical insights for exploiting segmentation-based visual abstractions more efficiently, resulting in reduced variance of learned policies. Examine the impact of different segmentation-based modalities on behavior cloning agents in the CARLA simulator. Discover how to optimize intermediate representations for driving tasks, moving beyond traditional image-space loss functions to maximize safety and distance traveled per intervention.

Introduction
Two dominating paradigms to selfdriving
Direct perception
Conditional forints learning
Intermediate representations
More Related Findings
What is a good visual abstraction
InputOutput
No Crash Benchmark
Identifying Relevant Classes
Results
Qualitative Results
Summary
Dataset Overview
Illustrations