From MIRAGE to CLEAR: Component-Level Explainable Anomaly Reasoning for Autonomous Vehicle Perception Systems

Jan 1, 2026· David FernandezDavid Fernandez ,Pedram MohajerAnsari ,Cigdem Kokenoz ,Amir Salarpour ,Bing Li ,Mert D. Pese
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Key Contributions & Takeaways

  • Unifies anomaly detection, violation classification, and component-level failure attribution into a single explainable framework (MIRAGE-CLEAR).
  • Decomposes complex perception attributions into highly interpretable, audit-ready reasoning chains compliant with the EU AI Act.
  • Achieves 95.2% accuracy in anomaly detection and 84% accuracy in component-level attribution on safety-critical perception tasks.
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Abstract

"LLaVA-7B and MoE-LLaVA identified potential crash scenarios 1.13 to 1.33 seconds earlier than human drivers, highlighting their potential role in autonomous driving systems."

Autonomous vehicles rely on perception systems with deep neural networks for traffic sign recognition (TSR), automated lane centering (ALC), and object detection (OD). While these systems perform well under standard conditions, perception failures trigger 17% of AV disengagements, yet only 4% receive clear causal attribution, impeding safety improvements. Emerging regulations, including the EU AI Act (2024), mandate transparency and component-level accountability for safety-critical AI systems, which current anomaly detection approaches cannot provide. We present MIRAGE-CLEAR, a framework combining dataset generation with systematic reasoning analysis. MIRAGE generates semantically rich driving scenarios by integrating 48,022 real-world scenes from major AV datasets with regulatory knowledge from the Manual on Uniform Traffic Control Devices (MUTCD). CLEAR, a three-layer LLM-based reasoning pipeline, decomposes attribution into interpretable subtasks: anomaly detection, violation classification, and component attribution. CLEAR achieves 95.2% accuracy in anomaly detection and 84% attribution accuracy for direct regulatory violations targeting TSR modules. The framework provides interpretable reasoning chains satisfying regulatory transparency requirements while enabling precise failure source identification. To the best of our knowledge, CLEAR represents the first system unifying detection, attribution, explainability, and regulatory compliance for AV perception diagnostics.
Type Conference paper
Venue IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2026)
Explainable AI Autonomous Vehicles Anomaly Detection Large Language Models
David Fernandez
Authors
David Fernandez
PhD Candidate in Computer Science

David Fernandez is a PhD candidate in Computer Science at Clemson University, working on safe, efficient, and explainable AI for safety-critical systems. His research spans perception, adversarial robustness, and on-device deployment of large foundation models, including LLMs and VLMs, with five first-authored publications on component-level explainability, zero-shot reasoning, and adversarial scenario analysis, alongside collaborative work on edge AI for industrial agentic systems. Much of this research is grounded in autonomous driving, where trustworthiness, latency, and robustness constraints are unforgiving, but the underlying methods transfer broadly to other high-stakes domains.

As a member of Clemson’s VIPR-GS Research Program, he develops hierarchical LLM reasoning frameworks and VLM evaluation systems for the U.S. Army’s Next Generation Combat Vehicle (NGCV) program, focusing on zero-shot reasoning and component-level explainability under real-world deployment constraints.

At BMW Group, he designs agentic AI systems for enterprise environments, building autonomous prompt optimization pipelines that enable continual agent improvement without model retraining and context-aware moderation frameworks that detect coordinated multi-turn adversarial attacks in production deployments.