Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models

• URL: http://arxiv.org/abs/2401.00988v1
• Date: Tue, 2 Jan 2024 01:54:22 GMT
• Title: Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models
• Authors: Xinpeng Ding and Jinahua Han and Hang Xu and Xiaodan Liang and Wei Zhang and Xiaomeng Li
• Abstract summary: We present NuInstruct, a novel dataset with 91K multi-view video-QA pairs across 17 subtasks. We also present BEV-InMLLM, an end-to-end method for efficiently deriving instruction-aware Bird's-Eye-View features.
• Score: 76.99140362751787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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% improvement on various tasks. We plan to release our NuInstruct for future research development.

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