Javier Del Egido
Papers
5
Total Citations
99
H-Index
4
About
Javier Del Egido is a leading researcher in autonomous driving systems, specializing in end-to-end autonomous vehicle pipelines, simulation-based validation, and real-time multi-object tracking. His most impactful work, "Train Here, Drive There," demonstrates how autonomous driving architectures can be trained in simulation and deployed in real-world scenarios, bridging the critical gap between virtual testing and physical implementation. With over 37 citations, this paper has become a foundational reference for researchers developing scalable autonomous driving solutions using the CARLA simulator and ROS framework. Del Egido's contributions extend to robust waypoint tracking controllers and bird's eye view multi-object tracking systems that achieve real-time performance while maintaining power efficiency—a crucial balance for production autonomous vehicles. His work on the NHTSA typology-based validation pipeline provides a standardized framework for testing autonomous driving systems across countless urban scenarios, significantly reducing development costs. Del Egido's research addresses the most challenging aspects of autonomous driving: perception reliability, control robustness, and safety validation. His innovative combination of Hungarian algorithms with Kalman filters for object tracking has advanced the field's ability to handle complex urban environments, making autonomous driving safer and more practical for real-world adoption.
Research Focus
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Top Papers
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