Incremental Evaluation of Distance-Staged Curriculum Learning and LiDAR-Based Runtime Shielding for USV Navigation in Constrained Nearshore Waters

Autonomous navigation in constrained nearshore waters is challenged by boundary constraints, static obstacles, and incomplete perception. Using Proximal Policy Optimization (PPO) as a common baseline, this study integrates distance-staged curriculum learning and a LiDAR-based Safety Shield into a point-to-point USV obstacle-avoidance framework and evaluates their incremental effects on training-task organization and runtime action filtering. Validation includes fixed-stratified paired tests, unseen obstacle layouts, three independent training runs, threshold-sensitivity analysis, and new cross-seed Shield evaluation on independently trained policies. Curriculum learning does not provide a consistent final success-rate advantage across the three training runs; its paired decision-efficiency effect is statistically detectable in the original run but practically modest. By contrast, the Safety Shield increases success and reduces collisions across all four independently trained seed-101/202 policies tested with Shield OFF/ON, while introducing a small number of timeouts and a small efficiency cost in triggered cases. Controlled runtime diagnostics further show that the Shield’s slow-forward command is a reduced-propulsion action rather than explicit braking. Overall, distance staging mainly changes training-task organization and produces modest decision-efficiency effects, whereas runtime shielding exhibits a reproducible trade-off between collision-risk mitigation and intervention cost within the present simplified Unity environment.