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News/DriveMLP: When Training Strategy Outperforms Model Complexity in Autonomous Driving
arxiv
PublishedOctober 22, 2026 at 4:00 AM

DriveMLP: When Training Strategy Outperforms Model Complexity in Autonomous Driving

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arXiv:2610.29676v1 Announce Type: new Abstract: Learning motion planning policies that remain reliable under closed-loop execution is a central challenge in Autonomous Driving (AD). Imitation Learning (IL) scales well to large AD datasets but suffers from distribution shift, causal confusion, and co

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