Human Interactive Driving Research Intern, Learning-Based Control for Safe Autonomous Driving

Within the Extreme Performance Intelligent Control team, we are developing techniques to advance vehicle safety and performance capabilities using tools from machine learning, optimization, and control theory. We pursue a hybrid approach to high-performance driving that unites physics-based optimal control with data-driven learning. By embedding vehicle dynamics and optimization structure into learned controllers, we aim to build systems that adapt to uncertainty and match or exceed expert drivers while retaining the interpretability and guarantees of classical methods.