AIBullisharXiv – CS AI · Mar 177/10
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What Matters for Scalable and Robust Learning in End-to-End Driving Planners?
Researchers introduce BevAD, a new lightweight end-to-end autonomous driving architecture that achieves 72.7% success rate on the Bench2Drive benchmark. The study systematically analyzes architectural patterns in closed-loop driving performance, revealing limitations of open-loop dataset approaches and demonstrating strong data-scaling behavior through pure imitation learning.