DriveMoE applies scene-specialized Vision MoE and skill-specialized Action MoE to a VLA baseline to achieve SOTA closed-loop performance on Bench2Drive.
Don’t shake the wheel: Momentum-aware planning in end-to-end autonomous driving
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 3representative citing papers
FocalAD adds an ego-local graph interactor and focal loss to prioritize decision-critical neighbors, yielding lower collision rates than prior methods on nuScenes, Bench2Drive, and especially the Adv-nuScenes robustness set.
A hybrid imitation-plus-reinforcement diffusion planner generates more diverse multi-mode trajectories for end-to-end autonomous driving, with a new diversity metric used for evaluation.
citing papers explorer
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DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
DriveMoE applies scene-specialized Vision MoE and skill-specialized Action MoE to a VLA baseline to achieve SOTA closed-loop performance on Bench2Drive.
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FocalAD: Local Motion Planning for End-to-End Autonomous Driving
FocalAD adds an ego-local graph interactor and focal loss to prioritize decision-critical neighbors, yielding lower collision rates than prior methods on nuScenes, Bench2Drive, and especially the Adv-nuScenes robustness set.
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DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving
A hybrid imitation-plus-reinforcement diffusion planner generates more diverse multi-mode trajectories for end-to-end autonomous driving, with a new diversity metric used for evaluation.