2 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
Disturbance Observer-based Control Barrier Functions with Residual Model Learning for Safe Reinforcement Learning
Dvij Kalaria, Qin Lin, John M. Dolan
Reinforcement learning (RL) agents need to explore their environment to learn optimal behaviors and achieve maximum rewards. However, exploration can be risky when training RL dire…
Spatio-temporal Motion Planning for Autonomous Vehicles with Trapezoidal Prism Corridors and Bézier Curves
Srujan Deolasee, Qin Lin, Jialun Li +1
Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free trajectories. A layered motion planning approach with decoupled path and speed planni…
Safe planning and control under uncertainty for self-driving
Shivesh Khaitan, Qin Lin, John M. Dolan
Motion Planning under uncertainty is critical for safe self-driving. In this paper, we propose a unified obstacle avoidance framework that deals with 1) uncertainty in ego-vehicle…
Safe Planning for Self-Driving Via Adaptive Constrained ILQR
Yanjun Pan, Qin Lin, Het Shah +1
Constrained Iterative Linear Quadratic Regulator (CILQR), a variant of ILQR, has been recently proposed for motion planning problems of autonomous vehicles to deal with constraints…