Accurate vehicle localization must be maintained even in tunnel sections where GNSS reliability is degraded. However, conventional GNSS/INS-based localization rapidly accumulates errors in such environments, affecting lane-level decision-making and path-following stability. To address this problem, this study proposes a dedicated localization support sign for stable LiDAR observation and a point-cloud-registration-based correction algorithm. The proposed method detects a dedicated sign using a PointPillars-based detector, and the corresponding point cloud is registered to a pre-built reference map to estimate a rigid correction transform online. The sign was installed in a tunnel section of a proving ground that reproduces real-road conditions. For evaluation, the driving sequence was analyzed by separating the pre-entry section, the tunnel section before dedicated-sign recognition, and the section after dedicated-sign recognition. The proposed pipeline substantially reduced localization error after dedicated-sign recognition, compared with the GNSS/INS-only baseline. The dedicated sign also provided more stable correction than ordinary tunnel structures within the same registration pipeline. These results indicate that the proposed LiDAR-based pipeline can suppress localization drift in GNSS-degraded sections.

As autonomous vehicle (AV) technologies advance, automated mobility-on-demand (AMoD) services, particularly demand-responsive transit (DRT) based shuttles, are emerging as a viable model for publicfacing deployment, yet in early-stage rollouts, adoption is shaped less by realized performance than by how prospective users perceive and prioritize service attributes under uncertainty. This study examines attribute prioritization across two service stages (pre-boarding and in-vehicle) and how these priorities vary with service familiarity and psychological concerns. A structured survey of 300 residents in Hwaseong City, South Korea, where an AMoD service is scheduled for implementation, asked respondents to rank eight service attributes (four per stage), with waiting-time-related attributes embedded in both stages to allow indirect comparisons. Using a hybrid rank-ordered logit model integrating ranked preferences, latent concern constructs, and observed familiarity measures, the analysis shows that psychological concerns and service familiarity explain preference variation more effectively than basic socio-demographics. Users familiar with DRT or concerned about adoption barriers prioritized coverage scalability and information accuracy, whereas those concerned about AV technology emphasized physical accessibility and driving stability. These support a phased deployment strategy that prioritizes expanding coverage and providing reliable information to reduce perceived entry barriers before enhancing performance-related attributes as familiarity grows.