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Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving
Beyond the dataset, we provide a simulation and evaluation framework that enables a planner's actions to be simulated in closed-loop to account for interactions with other traffic participants.
Risk-Bounded Control with Kalman Filtering and Stochastic Barrier Functions
Our goal is to design controllers that bound the probability of a system failure in finite-time to a given desired value.
Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances
Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems.
Gray-box Adversarial Testing for Control Systems with Machine Learning Component
Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics.
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