CogAD: 认知层次引导的端到端自动驾驶
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving
AI总结：
提出CogAD模型，通过全局到局部的层次感知和意图条件多模态轨迹生成，模拟人类驾驶员的认知层次机制，在nuScenes和Bench2Drive上实现端到端规划的最优性能，尤其在长尾场景和复杂真实驾驶条件下表现优异。
摘要：
尽管端到端自动驾驶取得了显著进展，但现有方法在感知和规划方面仍与人类认知原则存在根本性不一致。本文提出CogAD，一种新颖的端到端自动驾驶模型，模拟人类驾驶员的层次认知机制。CogAD实现了双层次机制：用于类人感知的全局到局部上下文处理，以及用于认知启发的规划的意图条件多模态轨迹生成。所提方法展示了三个主要优势：通过层次感知实现全面的环境理解，通过多层次规划实现鲁棒的规划探索，以及通过双层不确定性建模实现多样且合理的多模态轨迹生成。在nuScenes和Bench2Drive上的大量实验表明，CogAD在端到端规划中达到了最先进的性能，在长尾场景和复杂真实驾驶条件下表现出特别的优越性和鲁棒泛化能力。
英文摘要：
While end-to-end autonomous driving has advanced significantly, prevailing methods remain fundamentally misaligned with human cognitive principles in both perception and planning. In this paper, we propose CogAD, a novel end-to-end autonomous driving model that emulates the hierarchical cognition mechanisms of human drivers. CogAD implements dual hierarchical mechanisms: global-to-local context processing for human-like perception and intent-conditioned multi-mode trajectory generation for cognitively-inspired planning. The proposed method demonstrates three principal advantages: comprehensive environmental understanding through hierarchical perception, robust planning exploration enabled by multi-level planning, and diverse yet reasonable multi-modal trajectory generation facilitated by dual-level uncertainty modeling. Extensive experiments on nuScenes and Bench2Drive demonstrate that CogAD achieves state-of-the-art performance in end-to-end planning, exhibiting particular superiority in long-tail scenarios and robust generalization to complex real-world driving conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
