# A Method of Selecting Automated Driving Service Sections Considering Spatial Influence Factors
## 7 Conclusions  
One needs to consider two aspects when planning the operational design (ODD). First, car OEMs have their own ODDs mechanically when producing ADS-equipped vehicles; however, they cannot guarantee that they are in real road traffic environments because of unexpected spatial influence factors. Second, service providers who do not carry out OEMs should also have their own ODDs in their service sections to provide passengers with automated vehicle service safely. The proposed method considers these two aspects by reflecting the disengagement of ADS-equipped vehicles and the spatial influence factors in the target service section. The purpose of this study is to develop a method for selecting an automated driving service section that can be used when central and local governments plan to introduce services in the future. Since the central government and local governments to introduce automated driving services need to ensure inclusiveness and welfare, especially in public transportation, it is not reasonable to select a specific car OEM and its ODD in advance, but it is necessary to apply the proposed analysis method to the expected service sections. In this study, on the basis of the expected ODD, the spatial factors affecting the safety of automated driving were estimated with automated driving mode change (i.e., disengagement) data obtained from level 3 automated vehicles, which were actually operated with payment services in domestic urban areas. On the basis of those factors, the method of selecting the automated driving service section was proposed based on spatial information analysis. This study consists of three subparts as follows. First, the spatial factors that affect the safety of automated driving were estimated by using the field data obtained from the level 3 automated driving services in Korea. As a result, those factors were derived with a high probability of collision between dynamic factors, such as crosswalks, traffic lights, intersections, and bicycle roads. Second, the method of selecting automated driving service sections with spatial information analysis was proposed on the basis of those estimated factors, and then, the framework for selecting the automated driving service section was presented by synthesizing the procedures of the proposed method in this study. Third, the proposed analysis method was validated by applying it to a testbed of the automated driving car-sharing service project sponsored by the Korea Autonomous Driving Development Innovation Foundation (KADIF). As a measure of effectiveness (MOE), the matching rate between the service section derived through the proposed method and the demonstration section and target section in the target testbed (i.e., Jeju Advanced Science and Technology Complex) was compared and analyzed. As a result, in the level 3 automated driving car-sharing service, the matching rate of the planned section comparedto the target section was about 91% for the number of links and 94% for the section distance, and the matching rate of the demonstration section compared to the planned section was about 81% for the number of links and about 70% for the section distance. In level 4, the matching rate of the planned section compared to the target section was 100% consistent with both the number of links and the distance, and the matching rate of the demonstration section compared to the planned section was about 74% in the number of links and more than 84% in the section distance. Therefore, the analysis method proposed in this study proved that it is possible to select sections that can provide the service in advance in the planning stage before the deployment of the automated driving service. Notably, this study has two main features that contribute to automated vehicle deployments and their service applications, especially compared with previous studies.