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.

Determining the loop noise bandwidth and the coherent integration time is essential and important for the design of a reliable digital phase-locked loop (DPLL) in global navigation satellite system (GNSS) receivers. In general, designers set such parameters approximately by utilizing the well-known fact that the DPLL is stable if the normalized bandwidth, which is the product of the integration time and the noise bandwidth, is much less than one. However, actual limit points are not fixed at exactly one, and they vary with the loop filter order and implementation method. Furthermore, a lower limit on the normalized bandwidth may exist. This paper presents theoretical upper and lower limits for the normalized bandwidth of DPLL in GNSS receivers. The upper limit was obtained by examining the stability of DPLL with a special emphasis on the digital integration methods. The stability was investigated in terms of z-plane root loci with and without the consideration of the computational delay, which is a delay induced by the calculation of the discriminator and the loop filter. The lower limit was analyzed using the DPLL measurement error composed of the thermal noise, oscillator phase noise, and dynamic stress error. By utilizing the carrier-to-noise density ratio threshold which indicates the crossing point between the measurement error and the corresponding threshold, the lower limit of the normalized bandwidth is obtained.

This paper verifies the applicability of multiple Global Navigation Satellite Systems (GNSSs) and side lobe signal utilization in Space Service Volume (SSV), especially for Geostationary Earth Orbit (GEO) missions over the Korean region. Unlike the ground or terrestrial systems, various constraints of space exploration in SSV cause a problem when estimating position using GNSS. This is mainly due to the limit of GNSS signal availability where its dominant variables include altitude, side lobe issues, as well as longitude because of different constellations of several GNSS. The numerical simulation shows the effectiveness of additional side lobe signals from multi-GNSS. In addition, the effect of non-MEO satellites’ signals in SSV for different longitudes is presented.