Alternative fuel vehicles and smart cars are rapidly changing the automobile market, affecting vehicle maintenance. Among them, vehicle self-diagnosis and prediction functions, some of the technologies to be installed in next-generation vehicles, are important technologies that can change the business model of the current vehicle maintenance market. Technical research on these technologies continues, but few studies estimate consumer preference and the future economic value of the technology. Therefore, this study used a choice experiment and mixed logit model to identify the consumer preference structure for vehicle self-diagnosis and fault prediction functions and to derive their respective economic value for drivers and non-drivers, finding a preference (in monetary terms) of 4,961 KRW (4.19 USD) per month overall, 3,525 KRW (2.98 USD) for drivers, and 7,372 KRW (6.22 USD) for non-drivers. In addition, it was found that non-drivers preferred other vehicles' additional functions more than drivers did. The results of the analysis are expected to serve as a reference for decision-making by vehicle manufacturers, the maintenance industry, new smart car users, and governments that introduce smart cars.