What are the advantages and challenges of end-to-end learning approaches for autonomous driving?

End-to-end learning trains neural networks to directly map sensor inputs to control outputs, bypassing modular perception-planning-control pipelines. Advantages: Learns relevant features automatically without hand-engineering, captures complex interactions implicitly, simpler system architecture, and potentially better integration of information. Challenges: Requires massive training data covering all scenarios; interpretability is limited (black box); verifying safety is difficult; handling rare events requires special techniques (data augmentation, curriculum learning); and maintaining safety guarantees is harder than modular systems. Hybrid approaches combine learning with structured components (learned perception with rule-based planning) or use learning for specific sub-tasks. Production deployment requires addressing validation, certification, and failure mode analysis.