GL-TGS: Guarded Learning-Based Time-Gap Supervision for Adaptive MPC in Perception-in-the-Loop Autonomous Highway Driving

Abstract

Adaptive cruise control (ACC) tracks a time-gap reference that trades efficiency against spacing safety; in practice this gap is fixed offline, although the safest choice depends on the closing speed and on the reliability of the perception that supplies the lead-vehicle state. We present GL-TGS-v2, a guarded, interpretable supervisor that selects the time-gap reference of an adaptive model predictive controller (MPC) online from tracked lead-vehicle kinematics. The supervisor is a decision-tree distillation of a calibrated adaptive expert, wrapped by a perception-aware safety shield; its only authority is the time-gap reference, so it retrofits onto an existing controller without re-opening the inner loop. We evaluate it in closed loop on a frozen stack that couples a YOLO11 detector, a joint probabilistic data association (JPDA) tracker, and the adaptive MPC, with documented runtime and per-run provenance gates. In a hard lead-braking scenario (ten runs per method), GL-TGS-v2 matches the expert at a matched mean time gap and improves minimum relative distance over fuzzy and fixed-gap baselines by 3.3–5.2 m, with 95% confidence intervals excluding zero, lower target loss, and no collisions. Benign testing shows no regression, while shield ablation characterizes the guard as an auditable protective override.