Rubén Izquierdo Gonzalo is an Assistant Professor at the Department of Automation, University of Alcalá, Spain. He is affiliated with the INVETT research group (Intelligent Vehicles and Traffic Technologies), focusing on advancing autonomous driving systems. His doctoral work (2020) centered on predicting vehicle intentions for advanced autonomous driving, supervised by Dr. Miguel Angel Sotelo Vázquez and Dr. David Fernández Llorca. Research interests include autonomous driving, machine learning applications in traffic systems, human-vehicle interaction, and explainable AI for safety-critical systems. His work combines knowledge graphs, Bayesian inference, and deep learning techniques to address challenges like lane change prediction, pedestrian behavior analysis, and sensor failure modeling in real-world scenarios. Key contributions include developing real-world deployable prediction architectures, virtual reality-based human-vehicle interaction studies, and sensor fusion methods for autonomous systems. Notable projects involve the Prevention Dataset benchmark for intention prediction and the CAPformer model for pedestrian crossing action prediction. His publications emphasize predictive systems, safety validation, and human-centric autonomous driving solutions. Current efforts focus on bridging simulation and real-world testing gaps, digital twin technologies, and ethical considerations in autonomous vehicle decision-making.
