arXiv (OAI Expanded)
open access
LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving
Rapid advances in vision-language models (VLMs) have generated growing interest in their application to autonomous driving. A prevailing assumption is that successive VLM generations will continually improve driving performance and eventually outperform state-of-the-art methods. To systematically ex
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving#925044
- BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving#793721
- BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving#789717
- SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models#1049655
- LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies#973686
Record · ID 920810
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive