RAIL: Risk-Aware Human-in-the-Loop Framework with Adaptive Intrusion Response for Autonomous Vehicles

Autonomous vehicles must remain safe and effective when encountering rare long-tailed scenarios or cyber–physical intrusions during driving. We propose a risk-aware human-in-the-loop framework, RAIL, that explicitly closes the detect–respond–learn loop within a unified runtime architecture. RAIL introduces an interpretable Intrusion Risk Index (IRI) that fuses heterogeneous runtime cues, including curvature actuation integrity, time-to-collision proximity, and observation-shift consistency, through a weighted Noisy-OR to enable probabilistic, source-decomposed risk quantification at control rate. When risk rises, a contextual bandit selects cue-specific shields and a graded authority blending mechanism interpolates between nominal and safeguarded actions, preserving efficiency under low risk while enabling proactive containment under threat. RAIL further integrates dual reward shaping and risk-prioritized replay so that takeovers and near misses steer learning. Across MetaDrive and CARLA, RAIL matches expert-level success while reducing safety violations and operator interventions relative to RL, safe RL, offline and imitation baselines. Under Controller Area Network (CAN) injection and LiDAR spoofing, RAIL reduces attack success rates by up to 48% and lowers disengagement under attack by more than 50%, demonstrating that explicit multi-source risk modeling yields substantial robustness gains in safety-critical autonomy.