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It is undeniable that multiagent interaction behaviors in safety\u2010critical events are more complex and difficult to predict. To address this, an interaction mechanism based on generative adversarial networks training, named GACNet, aimed at effectively predicting multiagent interaction behaviors under potential collision risks is proposed. GACNet is a deep learning framework capable of learning and capturing complex interaction patterns between multiple agents from real vehicle trajectory data. In addition, A conflict analysis module, which analyzes the predicted future trajectories and assesses potential collisions to provide a more detailed characterization of the interaction behaviors in safety\u2010critical events is designed and incorporated. This design enables the model to predict vehicle trajectory behaviors in safety\u2010critical events more accurately, aligning them more closely with real\u2010world trajectory distributions. 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