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arXiv · 2507.23540

A Unified Perception-Language-Action Framework for Adaptive Autonomous Driving

Yi Zhang·Erik Leo Haß·Kuo-Yi Chao·Nenad Petrovic·Yinglei Song·Chengdong Wu·Alois Knoll
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Abstract

Autonomous driving systems face significant challenges in achieving human-like adaptability, robustness, and interpretability in complex, open-world environments. These challenges stem from fragmented architectures, limited generalization to novel scenarios, and insufficient semantic extraction from perception. To address these limitations, we propose a unified Perception-Language-Action (PLA) framework that integrates multi-sensor fusion (cameras, LiDAR, radar) with a large language model (LLM)-augmented Vision-Language-Action (VLA) architecture, specifically a GPT-4.1-powered reasoning core. This framework unifies low-level sensory processing with high-level contextual reasoning, tightly coupling perception with natural language-based semantic understanding and decision-making to enable context-aware, explainable, and safety-bounded autonomous driving. Evaluations on an urban intersection scenario with a construction zone demonstrate superior performance in trajectory tracking, speed prediction, and adaptive planning. The results highlight the potential of language-augmented cognitive frameworks for advancing the safety, interpretability, and scalability of autonomous driving systems.

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Yi Zhang, Erik Leo Haß, Kuo-Yi Chao, Nenad Petrovic, Yinglei Song, Chengdong Wu, Alois Knoll. 2025-07-31. A Unified Perception-Language-Action Framework for Adaptive Autonomous Driving. https://arxiv.org/abs/2507.23540

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