Research is under way to achieve autonomous driving (AD) on urban roads by 2020. This study focused on a right-turn situation at an intersection difficult to navigate even for human drivers. Many accidents have been reported when turning right due to poor visibility of traffic in an oncoming lane. An AD system is being developed that can recognize and avoid a potential risk of accident where another vehicle at an intersection causes poor visibility. The aim is to automate identification of vehicle locations and locations with poor visibility as well as decisions as to whether to decelerate, accelerate, or stop. As solutions, an obstacle detection frame (ODF) was created to identify the locations of other vehicles and a visibility detection frame (VDF) was created to identify the locations of blind spots. Detection frames are defined in an internal intersection map in the AD system. A binary search tree is used to recognize a potential risk of accident and to decide an avoidance operation automatically from the detection frame results. The performance of the control logic was validated by simulations and prototype vehicle tests. It was validated that the AD system can take appropriate action based on fundamental actions defined for each type of blind spot, which dynamically changes based on the relative positions of other vehicles. The results confirmed that when there is poor visibility due to a vehicle making a right turn, the AD system can slow down until visibility is improved and can accelerate the vehicle again after safe operation has been confirmed.