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arXiv · 2505.21581

CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving

Zhennan Wang·Jianing Teng·Canqun Xiang·Kangliang Chen·Xing Pan·Lu Deng·Weihao Gu
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Abstract

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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Zhennan Wang, Jianing Teng, Canqun Xiang, Kangliang Chen, Xing Pan, Lu Deng, Weihao Gu. 2025-05-27. CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving. https://arxiv.org/abs/2505.21581

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