模仿人类驾驶认知层次，实现更智能的端到端自动驾驶。
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving
- 分层感知：从全局到局部处理环境信息，类人理解场景。
- 多模态轨迹生成：在复杂场景中表现更优，长尾情况效果显著提升。
- 适合研究自动驾驶认知机制或追求高鲁棒性的开发者。
尽管端到端自动驾驶已取得显著进展，但现有方法在感知与规划上仍与人类认知原理存在根本偏差。本文提出 CogAD，一种模拟人类驾驶员分层认知机制的新型端到端自动驾驶模型。CogAD 采用双层分层结构：全局到局部的上下文处理用于类人感知，意图驱动的多模式轨迹生成用于认知启发式规划。该方法具有三大优势：通过分层感知实现全面环境理解；借助多层级规划增强规划探索能力；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 与 Bench2Drive 数据集上的大量实验表明，CogAD 在端到端规划任务中达到当前最优性能，尤其在长尾场景和复杂真实驾驶条件下展现出卓越的鲁棒泛化能力。
原文摘要 · Abstract (English)
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.
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