Vision-Language-Action (VLA) models for autonomous driving rely heavily on successful expert demonstrations, leaving model-specific failures underexploited. Learning from these failures is hindered by unreliable diagnoses, poorly matched correction targets, and coarse rewards. We propose RefineDrive, a failure-guided p...
Zhe Sun, Zi-Yin Luo, Ye-Hao Lu et al.· 0 citations
Average performance on routine driving benchmarks does not establish planner reliability under rare, safety-critical hazards. We proposed ExceptionDrive, a counterfactual planning benchmark that uses VLM-assisted screening, localized multi-view editing, and quality auditing to insert hazards into real nuScenes scenes w...
Zi-Yin Luo, Zhe Sun, Ye-Hao Lu et al.· 0 citations
V-Link is proposed, which explicitly recovers visual representations during the vision-language (VL) to action (A) feature transfer and injects them into Action DiT through asymmetric pathways.
Ye-Hao Lu, Jia-Rui Yang, Yu-Ning Su et al.· 0 citations
This work injects two complementary semantic priors into Visual prompt tuning, a cascaded scheme that integrates both priors throughout ViT adaptation, and proposes a cascaded scheme that integrates both priors throughout ViT adaptation.
Xi Xiao, Xing-Jian Li, Cheng Han et al.· Trans. Mach. Learn. Res.· 0 citations
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