Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles

Autonomous vehicles (AVs), particularly self-driving cars, have produced a large amount of interest in artificial intelligence (AI), intelligent transportation, and computer vision. Tracing and detecting numerous targets in real-time, mainly in city arrangements in adversarial environmental conditions, has become a significant challenge for AVs. The effectiveness of vehicle detection has been measured as a crucial stage in intelligent visual surveillance or traffic monitoring. After developing driver assistance and AV methods, adversarial weather conditions have become an essential problem. Nowadays, deep learning (DL) and machine learning (ML) models are critical to enhancing object detection in AVs, particularly in adversarial weather conditions. However, according to statistical learning, conventional AI is fundamental, facing restrictions due to manual feature engineering and restricted flexibility in adaptive environments. This study presents the explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection for autonomous vehicles (XAIFTL-AWCDAV) method. The XAIFTL-AWCDAV model's main aim is to detect and classify weather conditions for AVs in challenging scenarios. In the preprocessing stage, the XAIFTL-AWCDAV model utilizes a non-local mean filtering (NLM) method for noise reduction. Besides, the XAIFTL-AWCDAV model performs feature extraction by fusing three models: EfficientNet, SqueezeNet, and MobileNetv2. The denoising autoencoder (DAE) technique is employed to classify adverse weather conditions. Next, the DAE method's hyperparameter selection uses the Levy sooty tern optimization (LSTO) approach. Finally, to ensure the transparency of the model's predictions, XAIFTL-AWCDAV integrates explainable AI (XAI) techniques, utilizing SHAP to visualize and interpret each feature's impact on the model's decision-making process. The efficiency of the XAIFTL-AWCDAV method is validated by comprehensive studies using a benchmark dataset. Numerical results show that the XAIFTL-AWCDAV method obtained a superior value of 98.90% over recent techniques.