TRACER: An RDC-Triggered Trajectory Recovery Framework for Cyberattack-Resilient Adaptive Cruise Control

Abstract

The increasing connectivity of adaptive cruise control (ACC) systems exposes vehicle-state information to cyberattacks, potentially causing irrational car-following behavior and unsafe longitudinal dynamics. Existing trajectory recovery methods generally lack an interpretable criterion for recovery activation and remain vulnerable to attack-induced distribution shifts. To address these challenges, this paper proposes TRACER, a rational driving constraint (RDC)-triggered trajectory recovery framework for cyberattack-resilient ACC systems. TRACER employs a rationality-aware triggering (RAT) mechanism that activates recovery when the observed driving state violates the calibrated RDC bounds. It also introduces cross-branch robust imitation learning (CRIL), which combines a clean branch for learning nominal driving behavior with a noise-enhanced branch for improving robustness to corrupted observations. Cross-branch fine-tuning further aligns the robust recovery policy with the nominal driving-state distribution. Experiments across six cyberattack scenarios show that TRACER achieves reliable recovery triggering and outperforms state-of-the-art recovery baselines in terms of accuracy, stability, and safety. State-space analysis further confirms that TRACER mitigates attack-induced distribution shifts and restores coupled spacing-velocity dynamics, highlighting the effectiveness of robust policy learning for cyberattack-resilient ACC.