I conduct experiments in the Information and Decision Science Lab Scaled Smart City (IDS3C), a 1:25 scale robotic testbed located in Hollister Hall at Cornell University. The testbed replicates real-world urban traffic scenarios in a controlled environment, enabling us to validate control algorithms beyond simulation before transitioning to full-scale deployment.
My experimental work spans coordinating autonomous ground vehicles with quadcopters (Crazyflies), testing CAV interactions with human-driven vehicles (HDVs) through driving emulators, and validating signal-free intersection coordination — all using real-time optimal control frameworks with VICON motion capture localization.
Autonomous vehicles navigate a multi-lane intersection without traffic signals, coordinating through a decentralized optimal control framework. Each vehicle computes its trajectory in real time to cross the intersection safely and efficiently.
The experiment demonstrates energy-optimal merging and crossing maneuvers with provable safety guarantees, bridging the gap between simulation results and physical deployment.
Connected and automated vehicles (CAVs) coordinate alongside human-driven vehicles (HDVs) simulated through driving emulators. The framework handles the uncertainty of human behavior while maintaining safety and throughput.
This mixed-traffic scenario is critical for real-world deployment, where full automation penetration is unlikely in the near term.
Coordinating autonomous ground vehicles with Crazyflie quadcopters in a joint search-and-rescue mission scenario. Ground vehicles and aerial drones operate as a heterogeneous team, each assigned roles based on their sensing and mobility capabilities.
The multi-agent decision-making framework allocates tasks dynamically and re-plans in real time as the environment evolves, validated end-to-end in the scaled testbed.
Vehicles following the Intelligent Driver Model (IDM) respond to yield sign scenarios, testing the interaction between rule-based behavior and optimal control policies in a structured urban environment.
This experiment studies how IDM-governed vehicles integrate with infrastructure cues, informing the design of controllers that must coexist with legacy driving behavior.