Autonomous vehicles are equipped with multiple heterogeneous sensors and drive while processing data from each sensor in real time. Among the sensors, the global navigation satellite system (GNSS) is essential to the localization of the vehicle itself. However, if a GNSS-denied situation occurs while driving, the accident risk may be high due to the degradation of the vehicle positioning performance. This paper presents a cooperative positioning technique based on the lidar sensor and vehicle-to-everything (V2X) communication. The ego-vehicle continuously tracks surrounding vehicles and objects, and localizes itself using tracking information from the surroundings, especially in GNSS-denied situations. We present the effectiveness of the cooperative positioning technique by constructing a GNSS-denied case during autonomous driving. A numerical simulation using a driving simulator is included in the paper to evaluate and verify the proposed method in various scenarios.

In autonomous driving, place recognition is an essential component for successful mission execution, where Simultaneous Localization and Mapping (SLAM) is widely used in the system to estimate the vehicle's position by avoiding error accumulation. Various research efforts based on deep learning or handcrafted methods are currently underway to improve the place recognition performance. However, deep learning-based approaches pose certain challenges compared to handcrafted methods such as being less interpretable and often requiring extensive training time. In this paper, we propose a handcrafted method-based efficient feature extraction method for place recognition and point registration that aims to overcome these challenges while achieving high performance. The proposed method involves extracting robust feature points, generating powerful descriptors, and achieving performance levels similar to deep learning methods, while ensuring algorithmic versatility. Validation results using the KITTI dataset are also included in the paper, demonstrating exceptional performance even in demanding scenarios involving rotated loops. The results show an average loop closure accuracy of 95% or better, and an average pose estimation accuracy of 0.1 meters for translation and 0.15 degrees for rotation.

Autonomous driving simulators are an effective tool for developing autonomous driving algorithms so that they are widely used in research and development. However, the similarity of the virtual model to reality is closely related to the validity of the simulation results. Therefore, analyzing the characteristics of real sensors is necessary for mathematical modeling of virtual sensors in autonomous driving simulators. This paper presents a virtual lidar that has a high fidelity operating similarly to reality in the sensor modeling. The intensity variable factors which represents the strength of the received signal relative to the transmitted signal are effectively used for improving the fidelity of virtual lidar with a low computing power. The proposed virtual lidar is implemented in an autonomous driving simulator to show its feasibility by comparing with the existing virtual lidar.

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.

This study proposes a trajectory optimization method for racing vehicles, aiming to maximize speed and path planning performance by estimating tire friction coefficients. This method addresses the challenges inherent in high-speed racing environments, where tire friction and vehicle dynamics are critical for achieving optimal lap times. A friction circle model was used to determine the friction coefficient, which is subsequently applied to calculate the maximum feasible speed and acceleration constraints for different track segments. The optimization problem was formulated to minimize both path curvature and lap time, balancing these factors to determine the optimal trajectory. Experimental validation was conducted using a real vehicle on the AMG Speedway short track in Yongin, South Korea, where the proposed method demonstrated significant improvements in lap times across different tracks compared to the minimum curvature method. The proposed algorithm effectively minimized lap times and demonstrated applicability, with an average computation time of 30 s on a standard computer. This research provides valuable insights into autonomous vehicle path planning, particularly in racing contexts, and provides a robust framework for further advancements in autonomous driving technologies.