The National Highway Traffic Safety Administration asserted that distracted driving has become one of the leading causes of motor vehicle accidents，resulting in severe economic losses and casualties worldwide. Distracted driving refers to behaviors in which a driver’s attention shifts from the primary driving task to unrelated activities，impairing visual perception，cognitive awareness，decision-making，and operational control. Common distractions，such as calling，texting，eating，and conversing，are often covert，dynamic，and difficult to detect using a single sensing modality. Therefore，the development of a framework capable of robust perception，adaptive cognition，and real-time reasoning remains a major challenge in intelligent driving research. Embodied intelligence，a core concept of intelligent manufacturing，emphasizes deep interaction and tight coordination between intelligent agents and the physical environment. Moreover，embodied intelligence enables the emergence and evolution of cognition through continuous perception-action coupling，endowing intelligent systems with adaptive behavior generation，self-optimization，and environmental awareness. In recent years，embodied intelligence has been widely applied in bionic robotics，unmanned systems，and intelligent vehicles，driving innovation and theoretical progress in human-machine collaboration and cognitive autonomy. Building upon this theoretical foundation，this study systematically reviews the key technological pathways for distracted driving detection from the perspective of embodied cognition，focusing on frontier research trends，such as large-scale multimodal models，domain adaptation，and continual learning. This study introduces a hierarchical theoretical framework consisting of five interrelated layers that together form a closed-loop structure of perception，cognition，and execution by integrating the cognitive logic of embodied intelligence with the dynamic characteristics of complex traffic environments. The physical layer serves as the foundation，responsible for multimodal data acquisition through cameras，infrared sensors，microphones，and physiological sensors，ensuring a comprehensive coverage of driver behaviors and environmental conditions. The communication and fusion layer handles data transmission，synchronization，and multimodal fusion across heterogeneous devices，enabling reliable multi-source collaboration and adaptive information exchange under varying network and vehicular conditions. The data and knowledge layer manages and semantically interprets the collected information，constructs knowledge graphs of driver states and contextual events，and supports the integration of prior driving knowledge for enhanced reasoning. The cognition and reasoning layer applies advanced learning and inference mechanisms to interpret multimodal inputs，recognize driver states，predict behavioral intentions，and formulate context-aware strategies for distraction mitigation. Finally，the capability and application layer functions as the executive component，connecting cognitive outputs to real-world interaction，and supports human-machine communication through digital human interfaces，semantic prompts，and feedback control mechanisms for adaptive intervention and behavioral regulation. Together，these five layers establish a theoretical structure that transitions from passive perception to active cognition and embodied execution，forming an integrated mechanism for closed-loop understanding and intervention. Unlike existing detection systems that primarily focus on recognition and alerting，the proposed embodied-intelligence-based framework highlights a conceptual transformation toward human-centered adaptability，self-evolving learning，and knowledge-driven decision-making. This study provides a unified and extensible foundation that bridges multimodal perception，cognitive reasoning，and control feedback under the embodied intelligence paradigm as a theoretical contribution. The proposed framework is conceptual rather than engineering-oriented，serving as a blueprint for future empirical validation，experimental simulation，and real-world deployment. Meanwhile，the implementation of the proposed framework still faces considerable challenges. From the technical perspective，the constrained computational resources of in-vehicle terminals limit the deployment of complex deep learning models. Insufficient multimodal sensing and synchronization hinder the scalability of perception systems. Meanwhile，inference hallucination in large-scale reasoning models introduces uncertainty that threatens decision reliability and system safety. From the application perspective，ethical and legal controversies related to driver monitoring，data privacy，and algorithmic transparency remain unresolved，while high economic costs continue to impede large-scale deployment and commercialization. Future research on lightweight optimization，trustworthy and interpretable inference，distributed learning，and regulatory standardization is necessary to address these challenges. Such efforts are expected to ensure that embodied-intelligence-based distracted driving detection can evolve toward safe，efficient，transparent，and socially sustainable intelligent driving ecosystems. Overall，this study presents a systematic，forward-looking，and theoretically grounded exploration of embodied-intelligence-driven distracted driving detection. This study redefines the principles of human-machine collaboration，establishes a pathway for embodied cognitive understanding，and provides theoretical guidance for the evolution of next-generation intelligent and safe driving systems by consolidating fragmented advances in multimodal perception，driver behavior modeling，and adaptive decision-making into a unified theoretical architecture.