An Explainable IoT-Driven Framework for Trustworthy Decision-Making in Safety-Critical Autonomous Vehicles

Technical Summary: An Explainable IoT-Driven Framework for Trustworthy Decision-Making in Safety-Critical Autonomous Vehicles

Problem Statement
The deployment of autonomous vehicles (AVs) faces significant hurdles regarding trust, accountability, and regulatory compliance, primarily due to the "black box" nature of complex deep learning models. While these models offer high predictive accuracy, they lack transparency in their decision-making processes, particularly during safety-critical events such as emergency braking, obstacle avoidance, or ethical dilemmas. Furthermore, as AVs operate within interconnected Internet of Things (IoT) ecosystems involving diverse sensors and communication networks (V2V, V2I), the complexity of decision-making increases. Existing research often prioritizes predictive performance over interpretability or relies on post-hoc explanation techniques that may not be suitable for real-time, safety-critical environments. There is a critical need for a framework that balances predictive reliability with inherent explainability, decision traceability, and computational efficiency to ensure human-understandable justifications for autonomous actions.

Methodology
The study proposes a transparency-driven framework that integrates inherently interpretable machine learning models with explainability mechanisms to support real-time decision-making in IoT-enabled AVs. The methodology is structured into five stages:

1. Scenario Generation: A dataset of 450 synthetic driving scenarios was generated, equally distributed across five safety-critical categories: Sudden Obstacle, Ethical Decision-Making, Complex Traffic, Adverse Weather, and Mechanical Failure. These scenarios were derived from characteristics found in public datasets (e.g., nuScenes, CADC, Lost and Found) and ethical frameworks (e.g., Moral Machine).
2. Data Preprocessing: Scenarios were parameterized using variables such as vehicle speed, obstacle distance, traffic density, visibility, road surface conditions, and sensor reliability.
3. Model Development: The core decision-making engine utilizes inherently interpretable models: Decision Trees (for hierarchical logic mapping) and Linear Regression (for continuous variable relationships).
4. Explainability Analysis: To enhance interpretability and feature attribution, the framework incorporates post-hoc explanation techniques, specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations).
5. Evaluation: The framework was assessed using a comprehensive set of metrics:
   • Transparency Index (TI): Measures explanation completeness and clarity.
   • Decision Traceability Score (DTS): Evaluates the ability to reconstruct decision pathways.
   • Ambiguity Reduction Metric (ARM): Quantifies the reduction in decision uncertainty.
   • Computational Overhead (CO): Measures the additional latency introduced by explanation generation.
   • Predictive Performance: Assessed via accuracy and response latency.

Key Contributions
The paper contributes a practical foundation for developing trustworthy, regulation-ready autonomous vehicle systems through the following:

• Framework Development: An end-to-end explainable AI framework specifically designed for IoT-enabled AVs that integrates interpretable models with real-time constraints.
• Model Integration: The use of inherently interpretable models (Decision Trees, Linear Regression) combined with SHAP and LIME to provide both transparent decision pathways and quantified feature contributions.
• Comprehensive Evaluation: A rigorous evaluation across 450 diverse safety-critical scenarios, measuring not just accuracy but also transparency, traceability, ambiguity reduction, and computational efficiency.
• Balancing Act: A demonstration that high levels of explainability and accountability can be achieved without significantly compromising predictive performance or real-time operational speed.

Results
The experimental evaluation yielded the following findings:

• Predictive Performance: The framework achieved predictive accuracies ranging from 85.1% to 89.6% across all scenario categories.
• Real-Time Responsiveness: Response latencies remained consistently below 100 ms (ranging from 85 ms in sudden obstacle scenarios to 98 ms in adverse weather), satisfying real-time operational requirements.
• Transparency and Traceability: The framework achieved high Transparency Index scores (ranging from 8.2 to 8.8 on a 0–10 scale) and Decision Traceability Scores above 0.85.
• Ambiguity Reduction: The integration of explainability mechanisms reduced decision ambiguity by 27.9% to 31.5% compared to baseline models without explanation support.
• Computational Efficiency: The inclusion of SHAP and LIME introduced a minimal computational overhead, ranging from 6.9% to 9.2% (approximately 6–8 ms additional latency), confirming that transparency enhancements do not significantly hinder system responsiveness.

Significance
The paper posits that explainability should be treated as a fundamental design requirement rather than an optional post-processing feature for safety-critical autonomous systems. By providing clear, human-understandable justifications for autonomous actions, the proposed framework enhances accountability, facilitates regulatory compliance, and fosters public trust. The study demonstrates that it is possible to develop AV architectures that balance the competing demands of interpretability, efficiency, and predictive reliability. This work offers a practical pathway for integrating trustworthy AI into connected transportation ecosystems, ensuring that autonomous decisions are not only accurate but also auditable and aligned with human values and ethical principles.