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.

翻译：尽管端到端自动驾驶技术已取得显著进展，现有方法在感知与规划层面仍与人类认知原则存在根本性错位。本文提出CogAD——一种模拟人类驾驶员层次化认知机制的新型端到端自动驾驶模型。CogAD实现了双重层次机制：面向类人感知的全局到局部上下文处理，以及基于认知启发的意图条件多模态轨迹生成规划。该方法展现出三大核心优势：通过层次化感知实现全面环境理解，借助多级规划实现鲁棒的规划探索，以及通过双层级不确定性建模促进多样且合理的多模态轨迹生成。在nuScenes和Bench2Drive数据集上的大量实验表明，CogAD在端到端规划任务中达到最先进性能，尤其在长尾场景中表现突出，并对复杂现实驾驶条件展现出强大的泛化能力。