Partially automated driving systems still rely on human drivers to monitor the environment and resume control when needed. Due to reduced vigilance and situation awareness during automation, drivers may misjudge the level of risk and the system’s capability to handle sudden conflict scenarios, which can undermine overall safety. Effectively communicating risk-predictive information generated by the automation through the Human–Machine Interface (HMI) can help drivers better understand system behavior and adjust their trust accordingly. In this study, we conducted a 3 2 2 within-subjects experiment involving intersection conflict scenarios (HMI: baseline vs. risk-alert vs. multimodal alert; risk level: low vs. high; turning direction: left vs. right). Thirty participants took part in the simulation-based experiment, with psychological measures, peripheral biosignals, neural activity, and driver-initiated takeover behavior recorded throughout the experiment. Results showed that risk-predictive information amplified the difference in drivers’ subjective risk evaluations between high-risk and low-risk conditions, and promoted more effective cognitive control in high-risk scenarios. The multimodal alert HMI (integrating visual and auditory risk prediction information) increased situational trust and reduced takeover frequency. High-risk and right-turn conditions led to higher perceived risk, lower trust, and more frequent takeovers. These findings provide new insights into how risk-predictive HMI designs influence drivers’ performance in partially automated driving, contributing to safer and more effective human–automation interaction.