Renan Sebem
Papers
2
Total Citations
10
H-Index
2
About
Renan Sebem is a researcher at the forefront of intelligent transportation and robotic control systems, with a focus on making autonomous technologies both scalable and computationally efficient. His most notable contribution is a groundbreaking control architecture for Connected and Automated Vehicles (CAVs) that uniquely integrates scalable, distributed, and reconfigurable path planning—a work that has already garnered 7 citations since its 2023 publication. This architecture addresses a critical challenge in autonomous driving: enabling fleets of vehicles to adapt their routes dynamically while maintaining system-wide coordination. Sebem has also made significant strides in bridging theory and practice for robotic systems. His 2021 study on Model Predictive Control (MPC) provides a rigorous computational cost evaluation, comparing conventional MPC with its explicit counterpart (eMPC). By demonstrating how eMPC can overcome the computational bottlenecks that plague fast-dynamics robotics, this work offers a practical roadmap for deploying advanced control in real-time applications. Through these contributions, Sebem is helping to shape a future where both connected vehicles and robotic systems can operate with greater autonomy, speed, and reliability.
Research Focus
Key Achievements
Top Papers
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- 2