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All Journal International Journal of Robotics and Control Systems
Farag, Wael A.
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Control & Systems Engineering Electrical & Electronics Engineering

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Real-Time Pose Estimation for Autonomous Vehicles Using Probabilistic Landmark Maps and Sensor Fusion Farag, Wael A.; Fayed, Mohamed
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1851

Abstract

This study introduces a robust and accurate method for estimating autonomous vehicle position, facilitating safe navigation in urban and highway settings. The proposed technique employs a probabilistic particle filter framework, which, unlike approaches constrained by Gaussian assumptions, represents probability densities as samples, enabling more flexible position estimation. A key innovation lies in integrating a finely tuned Unscented Kalman Filter (UKF) to fuse radar and lidar data specifically for robust detection of pole-like static landmarks, whose positions and associated uncertainties are probabilistically modeled within an offline reference map. The particle filter leverages Bayesian filtering, associating UKF-derived landmark observations with this probabilistic map to refine the vehicle's pose. Broad simulation tests validate the method's effectiveness, achieving a mean localization error of approximately 11 cm in both longitudinal and lateral directions. Furthermore, the system demonstrates robustness, maintaining localization accuracy below 30 cm even with landmark position uncertainties up to 2 meters, and confirms real-time capability exceeding 100 Hz. These findings establish the approach as a reliable and precise solution for autonomous vehicle localization across various scenarios.
Real-Time Autonomous Vehicle Navigation via Rule-Based Waypoint Selection and Spline-Guided MPC Farag, Wael A.; Fayed, Mohamed
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1879

Abstract

This paper presents a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm aimed at improving autonomous highway navigation. LSPP uniquely combines localized quintic splines with a speed-profile optimizer to generate smooth, dynamically feasible trajectories that prioritize obstacle avoidance, passenger comfort, and strict adherence to road constraints such as lane boundaries. By leveraging real-time data from the vehicle’s sensor fusion module, LSPP accurately interprets the positions of nearby vehicles and obstacles, producing safe paths that are passed to the Model Predictive Control (MPC) module for precise execution. Simulations show LSPP reduces lateral jerk by 30% and computation time by 25% compared to Bézier-based methods, confirming enhanced comfort and efficiency. Extensive testing across diverse highway scenarios further demonstrates LSPP’s superior performance in trajectory smoothness, lane-keeping, and responsiveness over traditional approaches, validating it as a compelling solution for safe, comfortable, and efficient autonomous highway driving.
Co-Authors Fayed, Mohamed