A Novel Hybrid XAI Solution for Autonomous Vehicles: Real-Time Interpretability Through LIME-SHAP Integration

Tahir, Alayed, Hassan, Haider (2024) A Novel Hybrid XAI Solution for Autonomous Vehicles: Real-Time Interpretability Through LIME-SHAP Integration Sensors (Basel) (IF: 3) 24(21)

Abstract

The rapid advancement in self-driving and autonomous vehicles (AVs) integrated with artificial intelligence (AI) technology demands not only precision but also output transparency. In this paper, we propose a novel hybrid explainable AI (XAI) framework that combines local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP). Our framework combines the precision and globality of SHAP and low computational requirements of LIME, creating a balanced approach for onboard deployment with enhanced transparency. We evaluate the proposed framework on three different state-of-the-art models: ResNet-18, ResNet-50, and SegNet-50 on the KITTI dataset. The results demonstrate that our hybrid approach consistently outperforms traditional approaches by achieving a fidelity rate of more than 85%, interpretability factor of more than 80%, and consistency of more than 70%, surpassing the conventional methods. Furthermore, the inference time of our proposed framework with ResNet-18 was 0.28 s; for ResNet-50, it was 0.571 s; and that for SegNet was 3.889 s with XAI layers. This is optimal for onboard computations and deployment. This research establishes a strong foundation for the deployment of XAI in safety-critical AV with balanced tradeoffs for real-time decision-making.

Links

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548085
http://www.ncbi.nlm.nih.gov/pubmed/39517672
http://dx.doi.org/10.3390/s24216776

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