Felipe Arango
Papers
6
Total Citations
135
H-Index
6
About
Felipe Arango is a researcher specializing in autonomous driving systems, simulation frameworks, and robot operating system (ROS)-based software architectures. His work sits at the intersection of autonomous vehicle development, validation methodologies, and real-world deployment pipelines, with a particular focus on bridging the gap between simulated and physical driving environments. Arango's most recognized contribution, "Train Here, Drive There," has accumulated 37 citations and demonstrates how the CARLA simulator can be leveraged to validate fully autonomous driving architectures against real-world use cases. Complementing this, his waypoint tracking controller paper (31 citations) offers a modular, scalable solution for autonomous road vehicles operating within the ROS framework — a practical tool widely adopted by developers in the field. His 2022 work on building safe and reliable autonomous driving stacks (25 citations) addresses the critical engineering challenges of deploying AVs in highly dynamic environments. Beyond simulation and control, Arango has contributed to multi-object tracking using Bird's Eye View techniques and explored decision-making frameworks through Petri nets for urban scenarios. Together, his body of work — totaling over 130 citations — reflects a consistent commitment to making autonomous driving systems more robust, scalable, and verifiable for real-world application.
Research Focus
Key Achievements
Top Papers
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- 5Simulating use cases for the UAH Autonomous Electric Car14 citations · 2019
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