As the number of automobiles in use worldwide increases, the number of associated serious environmental and safety problems also increases. A solution to these problems is an autonomous platooning system for trucks, which is expected to have various effects such as reduction in carbon dioxide emissions and increase in traffic capacity. Although various control laws have been proposed for automated driving, control gains are typically tuned manually. The optimal values change owing to the freight or over a period of several years. Therefore, when the control performance decreases, gain tuning is again required. In this study, we propose a fuzzy inference-based self-tuning method of steering control gains, which leverages the relationship between the meander cycle/amplitude of a vehicle and control gains. Its effectiveness is experimentally evaluated.