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

2025-05-27

AI Summary

To address the fundamental misalignment between perception-planning decoupling in end-to-end autonomous driving and human cognitive principles, this paper proposes a human-inspired cognitive hierarchy. Our method features a global-local dual-level perception model for contextual awareness and an intention-conditioned multi-stage planning framework enabling intention-driven multimodal trajectory generation. We innovatively introduce dual uncertainty modeling—jointly quantifying perceptual confidence and planning intention entropy—to significantly enhance robustness in long-tail scenarios and cross-scenario generalization. Technically, the approach employs hierarchical Transformer encoding and intention-embedding-guided decoding. Evaluated on nuScenes and Bench2Drive, it achieves state-of-the-art end-to-end planning performance, outperforming existing methods notably under complex real-world traffic conditions.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Misalignment of autonomous driving with human cognition principles
• Need for hierarchical perception and planning in driving models
• Challenges in robust generalization to complex driving scenarios

Innovation

• Hierarchical cognition mechanisms for driving
• Global-to-local context processing perception
• Intent-conditioned multi-mode trajectory planning