@proceedings{bibcite_513,
  author = {Gamal Zayed and Ahmed Saeed and Yehea Ismail},
  title = {Enhancing Lidar-GPS Fusion for Localization: Integrating Ionospheric Modeling with NeQuick G and V2V Implementation},
  abstract = {Lidar (Light Detection and Ranging) provides high-resolution mapping and odometry for localization but faces limitations when used alone. Conversely, GPS positioning is impaired by ionospheric irregularities{\textemdash}such as Total Electron Content (TEC) variations, scintillation, and gravity waves{\textemdash}which distort satellite signals and reduce accuracy. This work explores a multi-modal fusion approach that integrates Lidar and GPS to optimize localization by compensating for ionospheric-induced errors. In the first phase, we focus on enhancing the GPS component of the Lidar-GPS fusion using the Galileo-based NeQuick G model, a mathematical framework for simulating ionospheric variations. Utilizing NASA{\textquoteright}s Archive of Space Geodesy Data, we analyze the spatio-temporal distribution of Vertical TEC (VTEC). By applying Particle Swarm Optimization (PSO), we identify global VTEC maxima at different altitudes, shedding light on worst-case ionospheric disturbances. For example, our analysis predicts that on January 1, 2027, at 12:00 AM UTC, the highest VTEC values occur at coordinates (-9.8, -88) near Peru, a critical region where GPS accuracy may degrade. This phase aids in forecasting GPS performance fluctuations in high-disturbance zones and informs the optimization of Lidar-GPS fusion for precision applications, including autonomous vehicles and V2V communication. The second phase is an aspirational extension aimed at implementing a Vehicle-to-Vehicle (V2V) communication model using Lidar sensors. This planned work will address challenges such as third-vehicle occlusion and background visible light noise. By integrating Lidar landmark matching, SLAM, and 3D point cloud processing, we intend to enhance localization reliability in dynamic multi-vehicle environments. Ultimately, this study bridges atmospheric science and vehicular localization, demonstrating the synergy between Lidar-based SLAM, ionospheric modeling, and the potential for robust Lidar-driven V2V networking to improve autonomous navigation and road safety.},
  year = {2025},
  journal = {HamSCI Workshop 2025},
  month = {03/2025},
  publisher = {HamSCI},
  address = {Newark, NJ},
  language = {eng},
}