Miguel Ortiz

Papers

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About

Miguel Ortiz is a leading researcher in autonomous driving systems, with a focused expertise in the safe and reliable implementation of Autonomous Driving Stacks (ADS) and motion prediction for self-driving vehicles. His most impactful work, "How to build and validate a safe and reliable Autonomous Driving stack? A ROS based software modular architecture baseline" (2022, 25 citations), addresses one of the most critical engineering challenges of our era: developing autonomous vehicles that can operate with greater reliability than human drivers in highly dynamic environments. Ortiz proposes a modular, ROS-based software architecture that provides a foundational baseline for building and validating ADS, offering a practical framework for ensuring system safety and robustness. In parallel, his research on "Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction" (2022, 6 citations) tackles the crucial task of predicting the future trajectories of multiple agents in complex environments, from social robots to self-driving cars. By leveraging map-based features and attention mechanisms, Ortiz advances end-to-end network approaches that render top-view scenes and past trajectories, improving the efficiency and accuracy of motion prediction. His work is instrumental in bridging the gap between theoretical autonomy and real-world deployment, making him a key contributor to the future of safe autonomous mobility.