With the rapid development of artificial intelligence in the field of autonomous driving, the way we drive is undergoing significant transformation. Currently, the application of Trajectory-guided Control Prediction (TCP) for End-to-End Autonomous Driving enables real-time prediction of obstacle motion trajectories and provides early warning information through Human-Machine Interface (HMI) display. This potentially enhances users’ visual perception of autonomous vehicles. However, there is still no clear answer regarding how the transparency of TCP visualizations should be configured and how such configurations affect users’ visual perception. To address this gap, this study explores the impact of different transparency levels in TCP visualizations on users’ visual perception from a visual cognition perspective. Using a between-subjects experimental design with one-way ANOVA, the study examines the effects of TCP visualizations with transparency levels of 20%, 40%, 60%, and 80% on users’ visual perception. A total of 100 participants were recruited through convenience sampling and completed questionnaires and interviews. The results reveal that: The transparency of TCP visualizations in autonomous driving HMI significantly affects users’ visual perception. High-transparency TCP visualizations result in lower levels of user experience compared to low-transparency designs. TCP visualizations with 40% transparency outperform those with 20%. The results of this study can provide a valuable reference for the visualization and design of TCP in HMI in autonomous driving scenarios.