To sort out the research context of intelligent driving decision-making in complex traffic scenarios, this paper conducts a systematic induction and analysis from three core dimensions: scenario classification, decision-making algorithms, and decision-making indicators based on existing research results. According to the differences in influencing factors, complex traffic scenarios are divided into three categories: complex vehicle behavior scenarios, high-density traffic flow scenarios, and complex structural road scenarios, and the risk characteristics of each type of scenario are analyzed. Combined with scenario characteristics, decision-making algorithms are classified into learning-based, interactive, and end-to-end types, and the advantages and limitations of different algorithms in practical applications are compared and analyzed. Decision-making indicators are sorted out from three aspects: safety, comfort, and efficiency, and the core focus indicators under different scenarios are clarified. Through the above analysis, this paper summarizes the key directions of current research, and at the same time points out three major research difficulties, namely insufficient research on decision-making in compound complex scenarios, lack of self-evolutionary decision-making mechanisms, and inconsistent decision-making evaluation indicators. Based on this, the paper proposes three future research directions, including the construction of multi-objective decision-making models, the development of self-learning decision-making algorithms, and research on cognition-based decision-making, aiming to provide theoretical support and practical references for intelligent driving decision-making research in complex traffic scenarios and future dilemma scenarios.