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An Efficient Risk-aware Branch MPC for Automated Driving that is Robust to Uncertain Vehicle Behaviors
One of the critical challenges in automated driving is ensuring safety of automated vehicles despite the unknown behavior of the other vehicles.
Automated Lane Merging via Game Theory and Branch Model Predictive Control
To overcome the challenge of multi-modal driving behavior exhibited by the surrounding vehicles, we formulate the trajectory selection as a matrix game and compute some equilibrium solutions.
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions
Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots.
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