Conditionally automated vehicles still rely on drivers to retake control when something unexpected emerges. We cataloged hazard types from naturalistic driving data and used those insights to design a gaze-guidance takeover aid grounded in the N-SEEV attention model. In a driving simulator study, the highly salient guidance cut secondary-hazard collisions during takeovers, indicating that directing eyes toward relevant risks can improve transition safety.