More than 2 million motor vehicles are exported from the Republic of Korea annually and most of them are transported by pure car and truck carriers (PCTCs), vessels designed for the transport of motor vehicles. These vessels are equipped with multiple decks and ramps, enabling efficient loading and unloading of motor vehicles. However, loading and unloading of motor vehicles poses a particular challenge, as each motor vehicle has to be loaded and unloaded individually. In current practice, a team of drivers brings a vehicle on or off board leading to significant operational inefficiencies and increased costs. With the development of sensor and autonomous vehicle technology, vehicles are sensing their environment and performing some portions of driving tasks without human involvement. This new technology is expected to improve the current transportation and logistics system. For example, a fully automated vehicle system can replace human drivers in the loading and unloading of motor vehicles, improving the operational efficiency of PCTCs. In this study, we investigate the operational improvement when the conventional loading task is switched to autonomous driving. A microscopic traffic simulation program is utilized to represent the loading of vehicles on a PCTC with autonomous vehicle technology. The total time required to load one deck of the PCTC is compared among loading methods used to investigate the operational improvement when the autonomous vehicle technology is introduced under different operating conditions including vehicle speed and vehicle headway.