Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models
CoRR(2024)
摘要
The rise of multimodal large language models (MLLMs) has spurred interest in
language-based driving tasks. However, existing research typically focuses on
limited tasks and often omits key multi-view and temporal information which is
crucial for robust autonomous driving. To bridge these gaps, we introduce
NuInstruct, a novel dataset with 91K multi-view video-QA pairs across 17
subtasks, where each task demands holistic information (e.g., temporal,
multi-view, and spatial), significantly elevating the challenge level. To
obtain NuInstruct, we propose a novel SQL-based method to generate
instruction-response pairs automatically, which is inspired by the driving
logical progression of humans. We further present BEV-InMLLM, an end-to-end
method for efficiently deriving instruction-aware Bird's-Eye-View (BEV)
features, language-aligned for large language models. BEV-InMLLM integrates
multi-view, spatial awareness, and temporal semantics to enhance MLLMs'
capabilities on NuInstruct tasks. Moreover, our proposed BEV injection module
is a plug-and-play method for existing MLLMs. Our experiments on NuInstruct
demonstrate that BEV-InMLLM significantly outperforms existing MLLMs, e.g.
around 9
future research development.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要