Most of recent studies indicate that people are negatively predisposed toward utilizing autonomous systems. These findings highlight the necessity of conducting research to better understand the evolution of trust between humans and growing autonomous technologies such as self-driving cars (SDC). This research therefore presents a new approach for real-time trust measurement between passengers and SDCs. We utilized a new structured data collection approach along with a virtual reality (VR) SDC simulator to understand how various autonomous driving scenarios can increase or decrease human trust and how trust can be rebuilt in the case of incidental failures. To verify our methodology, we designed and conducted an empirical experiment on 50 human subjects. The results of this experiment indicated that most subjects could rebuild trust during a reasonable timeframe after the system demonstrated faulty behavior. Furthermore, we discovered that the cultural background and past trust-related experiences of the subjects affect how they lose or regain their trust in SDCs. Our analysis showed that this model is highly effective for collecting real-time data from human subjects and lays the foundation for more-involved future research in the domain of human trust and autonomous driving.