Posts

Present a real-time fail-operational controller for autonomous driving in the presence of time-varying environmental disturbances. This controller is designed to guide autonomous vehicles back to a predefined safe state asymptotically, while upholding task efficiency.

Present a real-time parallel trajectory optimization method for the autonomous vehicles to achieve high travel efficiency in dynamic and congested environments.

We propose a learning-based tracking control scheme based on a feedback linearization controller and Gaussian Processes.

Propose a new learning-based framework based on incremental bayesian learning and control theory.

A learning-based safety-preserving cascaded quadratic programming control policy for safe trajectory tracking under wind disturbances.

Time reallocation for trajectory replanning.

A learning-based safety-preserving control policy for connected cruise control systems under model uncertainties in a mixed traffic flow.