Closed-Loop Refinement and Execution for Learned Driving Planners
Learning-based driving planners often fail during closed-loop execution because planning errors accumulate over time. This research introduces Closed-Loop Refinement and Execution (CLRE), a hierarchical control framework that refines a frozen planner's plan. It uses an optimization process to select feasible trajectories while ensuring safety checks, significantly improving driving scores and route completion.