A Phase-Aware Prediction-Horizon Policy for Learned-Cost CEM-MPC Lane-Change Planning
Model Predictive Control (MPC) offers a structured approach for autonomous-vehicle motion planning by optimizing predicted vehicle behavior over a finite horizon. As learning-based components become increasingly integrated into autonomous systems, interpretable interfaces between decision-making and control become increasingly important. Motivated by the need to examine how predictive depth should vary with maneuver context, this paper proposes and evaluates an adaptive phase-aware horizon-selection formulation within a hybrid Maximum Entropy Deep Inverse Reinforcement Learning-Model Predictive Control (MEDIRL-MPC) framework. The formulation conditions prediction depth explicitly on the recognized maneuver phase, providing an interpretable scheduling signal rather than maintaining the horizon as a globally fixed controller parameter. The considered architecture combines a MEDIRL-informed driving cost, a sampling-based Cross-Entropy Method planner, a kinematic vehicle model, and a scenario manager that identifies the current lane-change phase and provides the corresponding reference information. Controlled experiments are conducted in the CARLA simulator using a static-obstacle lane-change scenario. Fixed-horizon baselines, phase-wise analysis, common-state counterfactual comparisons, and controlled robustness experiments are used to evaluate the effect of prediction depth. The results indicate that the prediction horizon length greatly affects closed-loop behavior and computational demand, and that the relative suitability of different horizons varies across each scenario phase. The phase-aware policy further demonstrates that predictive depth can be allocated selectively across maneuver phases while preserving successful maneuver execution, and remains successful and lane-safe under controlled variations in target speed, obstacle distance, and activation distance. These findings support maneuver phase as an interpretable context for prediction-horizon adaptation within the evaluated architecture, while limiting the conclusions to the investigated scenario and experimental setting.