This paper presents a novel methodology for assessing the vulnerabilities of autonomous vehicles (AVs) across diverse operational design domains (ODDs) related to road transportation infrastructure, categorized by the level of service (LOS). Unlike previous studies that primarily focused on the technical performance of AVs, this study addressed the gap in understanding the impact of dynamic ODDs on driving safety under real-world traffic conditions. To overcome these limitations, we conducted a microscopic traffic simulation experiment on the Sangam autonomous mobility testbed in Seoul. This study systematically evaluated the driving vulnerability of AVs under various traffic conditions (LOSs A–E) across multiple ODD types, including signalized intersections, unsignalized intersections, roundabouts, and pedestrian crossings. A multivariate analysis of variance (MANOVA) was employed to quantify the discriminatory power of the evaluation indicators as the traffic volume was changed by ODD. Furthermore, an autonomous driving vulnerability score (ADVS) was proposed to conduct sensitivity analyses of the vulnerability of each ODD to autonomous driving. The findings indicate that different ODDs exhibit varying levels of sensitivity to autonomous driving vulnerabilities owing to changes in traffic volume. As the LOS deteriorates, driving vulnerability significantly increases for AV–bicycle interactions and AV right turns at both signalized and unsignalized intersections. These results are expected to be valuable for developing scenarios and evaluation systems to assess the driving capabilities of AVs.

This study aimed to develop a comprehensive validation methodology for an Infra-guidance system, which is an infrastructure-based service aimed at enhancing the safety of autonomous driving. The proposed method includes quantitative techniques for validating both the Infra-guidance algorithm module and the guidance message module using each optimal indicator. In addition, a promising method is suggested to validate the entire system by applying a multicriteria decision methodology. The relative weight for the algorithm module was higher than relative weight for the message module. Moreover, the relative weight of the latency for the message module was slightly higher than weight of the packet error rate. The proposed methodology is applicable for validating the performance of infrastructure-based services for enhancing connected autonomous driving based on the comprehensive quantification of various factors and indicators.

PURPOSES : An automated driving guidance framework was developed for automated vehicles based on cooperation between infrastructure and automated vehicles. The proposed automated driving guidance framework is assumed to function only when an automated vehicle encounters situations in which it cannot safely pass through without cooperation with the infrastructure. METHODS : A four-step concept of automated driving guidance levels was employed, and the decision criteria, such as moving object, event, and externality, were defined as the criteria for determining the automated driving guidance level. The judgment criteria of each stage and procedure for determining the autonomous driving guidance level were determined based on successive judgments, and the proposed automated driving guidance framework was designed based on an expert survey. The survey was aimed at experts with experience related to automated driving system research or technology development. RESULTS : The resulting framework shows the steps and criteria for determining whether automated driving guidance is required under a specific situation and what the guidance should be. CONCLUSIONS : The proposed automated driving guidance framework is designed to function only when an automated vehicle encounters situations in which it cannot safely pass through without cooperation with the infrastructure.