港中文中稿ICCV'25的自驾自适应快慢双系工作统AdaDrive

Core Viewpoint - The article discusses the introduction of AdaDrive, an adaptive slow-fast framework for integrating large language models (LLMs) into autonomous driving systems, aiming to balance high reasoning capabilities with real-time performance [2][3][4]. Background Review - Autonomous driving has been a research focus in academia and industry, with the emergence of LLMs enhancing cognitive reasoning and decision-making capabilities in driving systems. Early methods like LMDrive and AD-H faced challenges with memory overhead and latency, particularly in dynamic driving environments [4][7]. AdaDrive Algorithm Overview - AdaDrive is proposed as a next-generation framework that employs a fast-slow system paradigm, balancing high-frequency low-latency tasks with low-frequency high-reasoning tasks. It dynamically determines when to activate LLMs and adjusts their contribution based on scene complexity and prediction confidence [8][10][15]. Key Innovations - The framework introduces two key innovations: adaptive LLM activation, which learns the optimal activation timing through a novel loss function, and dynamic LLM contribution adjustment, which uses confidence-driven strategies to modulate LLM influence [8][9][21]. Experimental Results - AdaDrive demonstrated superior performance in the LangAuto benchmark, achieving driving scores of 80.9% and 70.6% in short-distance tasks, significantly outperforming the second-best method by 12.9% and 16.3% respectively [31][32]. - The method also showed advantages in inference time and memory costs due to its adaptive architecture and custom memory buffer, reducing computational overhead while enhancing driving performance [33]. Conclusion - The research highlights the potential of LLM-based language-guided autonomous driving technology, focusing on optimal activation timing and effective utilization strategies. AdaDrive's adaptive architecture and efficient memory management strategies significantly improve both effectiveness and efficiency compared to existing methods [43].