In the field of conditional autonomous driving technology, driver perceived risk prediction plays a crucial role in reducing traffic risks and ensuring passenger safety. This study introduces an innovative perceived risk prediction model for human-machine interaction in intelligent driving systems. The model aims to enhance prediction accuracy and, thereby, ensure passenger safety. Through a comprehensive analysis of risk impact mechanisms, we identify three key categories of factors, both subjective and objective, influencing perceived risk: driver's personal characteristics, ego-vehicle motion, and surrounding environment characteristics. We then propose a deep-learning-based risk prediction network that uses the first two categories of factors as inputs. The network captures the interactive relationships among traffic participants in dynamic driving scenarios. Additionally, we design a personalized modeling strategy that incorporates driver-specific traits to improve prediction accuracy. To ensure high-quality training data, we conducted a rigorous video rating experiment. Experimental results show that the proposed network achieves a 10.0% performance improvement over state-of-the-art methods. These findings suggest that the proposed network has significant potential to enhance the safety of conditional autonomous driving systems.