World knowledge-enhanced Reasoning Using Instruction-guided Interactor in Autonomous Driving

• URL: http://arxiv.org/abs/2412.06324v3
• Date: Thu, 02 Jan 2025 04:14:58 GMT
• Title: World knowledge-enhanced Reasoning Using Instruction-guided Interactor in Autonomous Driving
• Authors: Mingliang Zhai, Cheng Li, Zengyuan Guo, Ningrui Yang, Xiameng Qin, Sanyuan Zhao, Junyu Han, Ji Tao, Yuwei Wu, Yunde Jia,
• Abstract summary: We propose a framework, which aims to improve autonomous driving performance under perceptionlimited conditions. Specifically, we propose a plug-and-play instruction-guided interaction module that bridges modality gaps. To better integrate world knowledge with driving-related tasks, we have collected and refined a large-scale multi-modal dataset.
• Score: 39.153593828318215
• License:
• Abstract: The Multi-modal Large Language Models (MLLMs) with extensive world knowledge have revitalized autonomous driving, particularly in reasoning tasks within perceivable regions. However, when faced with perception-limited areas (dynamic or static occlusion regions), MLLMs struggle to effectively integrate perception ability with world knowledge for reasoning. These perception-limited regions can conceal crucial safety information, especially for vulnerable road users. In this paper, we propose a framework, which aims to improve autonomous driving performance under perceptionlimited conditions by enhancing the integration of perception capabilities and world knowledge. Specifically, we propose a plug-and-play instruction-guided interaction module that bridges modality gaps and significantly reduces the input sequence length, allowing it to adapt effectively to multi-view video inputs. Furthermore, to better integrate world knowledge with driving-related tasks, we have collected and refined a large-scale multi-modal dataset that includes 2 million natural language QA pairs, 1.7 million grounding task data. To evaluate the model's utilization of world knowledge, we introduce an object-level risk assessment dataset comprising 200K QA pairs, where the questions necessitate multi-step reasoning leveraging world knowledge for resolution. Extensive experiments validate the effectiveness of our proposed method.

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