Autonomous driving in unstructured environments faces challenges such as missing road boundaries, terrain variations, random obstacle distributions, and complex vehicle–terrain interactions, making it difficult to achieve safe navigation by relying on lane-level priors from structured roads. To address the problems of the relative separation between traversability analysis and trajectory planning, the ineffective propagation of perception uncertainty, and the insufficient scene adaptability of coupling mechanisms, this paper takes traversability as the main thread and systematically reviews the research progress of perception–planning coupling mechanisms in unstructured environments. First, traversability analysis methods based on geometric terrain, semantic understanding, and physical dynamics are reviewed, and the representation and propagation mechanisms of uncertainty in the perception–planning chain are analyzed. Second, the role of traversability information in global path search, local trajectory optimization, and data-driven planning is discussed, and the applicable boundaries of different coupling architectures are summarized from the perspectives of representation level and system organization form. Finally, datasets, simulation platforms, and evaluation metric systems are summarized, and a risk-state-oriented adaptive perception–planning coupling framework is proposed to dynamically adjust coupling strength based on risk-state information, thereby improving the safety, interpretability, and environmental adaptability of autonomous driving in unstructured environments.