MME-RealWorld is the largest manually annotated high-resolution benchmark for MLLMs, where even the best models achieve less than 60% accuracy on challenging real-world tasks.
Embodied understanding of driving scenarios
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
AlphaDrive uses GRPO-based RL rewards and two-stage SFT+RL training on VLMs to improve autonomous driving planning performance and efficiency while producing emergent multimodal capabilities.
Introduces PKL to rank planning-critical occluded agents, creates a VLM-annotated benchmark on nuScenes, and shows fine-tuning on this data improves performance ~30% over random selection with smaller models outperforming larger zero-shot counterparts.
Senna decouples language-based high-level planning from an LVLM with low-level trajectory prediction from an E2E model, reporting 27% lower planning error and 33% lower collisions after pre-training on DriveX and fine-tuning on nuScenes.
citing papers explorer
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MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
MME-RealWorld is the largest manually annotated high-resolution benchmark for MLLMs, where even the best models achieve less than 60% accuracy on challenging real-world tasks.
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AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning
AlphaDrive uses GRPO-based RL rewards and two-stage SFT+RL training on VLMs to improve autonomous driving planning performance and efficiency while producing emergent multimodal capabilities.
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What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models
Introduces PKL to rank planning-critical occluded agents, creates a VLM-annotated benchmark on nuScenes, and shows fine-tuning on this data improves performance ~30% over random selection with smaller models outperforming larger zero-shot counterparts.
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Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
Senna decouples language-based high-level planning from an LVLM with low-level trajectory prediction from an E2E model, reporting 27% lower planning error and 33% lower collisions after pre-training on DriveX and fine-tuning on nuScenes.