Recent research:
The researcher’s work integrates autonomous systems, perception, and sustainable mobility to advance safe, efficient, and intelligent transportation. Across publications, the core focus lies in creating precise mapping and localization for autonomous vehicles, robust object and action recognition (including traffic officers and gestures) through advanced computer vision and AI, and optimizing energy use in electric mobility. Methodologies span LiDAR-based odometry and mapping (LIO-SAM), modern map generation for Autoware environments, YOLO-based object detection with multi-stage training, deep learning and LSTM for gesture interpretation, and energy efficiency analysis using real-world driving data, model-based control, MPC concepts, and meta-learning for state-of-charge prediction. The impact emphasizes enhanced navigation accuracy in challenging terrains (Cuenca, Ecuador), safer AV interaction with law enforcement, improved traffic officer detection for AV safety, and informed policy and urban planning insights for AV integration and electrification strategies. Overall, the work advances practical AV perception, map reliability, and energy-aware autonomous mobility solutions with potential benefits for safety, urban sustainability, and policy guidance.