Validation and verification (V&V) of autonomous vehicles in real-world testing are limited by time, cost, safety, and scalability. Driving simulators provide safer alternatives but often fail to reproduce actual vehicle dynamics. This paper presents a Vehicle-in-the-Loop (VIL) simulation framework that integrates a chassis dynamometer with a virtual driving simulator to emulate real vehicle responses in real time. The framework enables bidirectional communication between the autonomous driving controller and simulated sensor data. For ADAS-level sensors (e.g., radar, ultrasonic), Over-the-Air (OTA) stimulation converts virtual outputs into physical signals. However, OTA becomes impractical for high-bandwidth sensors such as LiDAR and multi-camera systems due to data complexity and transmission latency. To overcome these challenges, a data injection approach is adopted, directly feeding simulated sensor data into the controller through a custom packet interface. Scenario-based V&V experiments aligned with national traffic regulations demonstrate that the proposed framework achieves realistic, regulation-compliant testing and quantitative consistency with real-world driving, achieving reliable validation in complex scenarios.

For Advanced Air Mobility (AAM) systems operating in diverse environments, redundant localization techniques are essential to ensure continuous and safe mission execution. In this study, we propose a 3D place recognition and pose estimation method for AAM using a hemispherical light detection and ranging (LiDAR) sensor. The proposed approach includes a feature extraction method that leverages height differences in surrounding objects, a method for generating local and global descriptors from feature distances, and an efficient geometric verification and localization process through correspondence calculation. Additionally, the method incorporates a process to create a virtual descriptor database using a point cloud map, enabling robust localization in unvisited areas. All procedures are handcrafted, and the performance of the proposed method is validated through comparison with state-of-the-art methods using datasets generated in a simulator. The proposed method achieved over 99.16% average precision (AP) and a 99.99% F1 score in loop closure detection. In pose estimation, it achieved a root mean square error (RMSE) of 0.836 meters or less for position and 0.195 degrees or less for heading. Furthermore, a time analysis on both a general PC and an embedded device confirmed the real-time capability of the proposed method, with an average pose estimation time of 21.70 milliseconds on the embedded device, demonstrating its feasibility for real-time localization in low-power environments.