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This review comprehensively investigates the current state and emerging trends of autonomous vehicle terrain detection and segmentation. By systematically reviewing literature from various databases, this study outlines the evolution of detection and segmentation techniques from traditional computer vision methods to advanced machine learning and deep learning approaches. It identifies critical technological advancements, evaluates their performance, and discusses the challenges faced under various environmental conditions, data acquisition, and integration with vehicle systems. This study also highlights the need for standardized benchmarks and datasets to facilitate the development and testing of robust terrain detection systems. This review encompasses terrain detection and segmentation in structured environments, such as urban roads and highways, and unstructured environments, including rural paths and off-road terrains, to comprehensively analyze autonomous vehicle navigation challenges. By analyzing recent research findings, this review provides insights into future directions for overcoming these limitations and fostering innovation in the autonomous driving domain. • Explores terrain detection and segmentation technologies in autonomous vehicles. • Evaluates current methods' effectiveness and limitations across environments. • Identifies challenges in advancing terrain detection and segmentation approaches. • Highlights trends and solutions for future research directions. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence refers to developing artificial intelligence models and systems that can provide clear, understandable, and transparent explanations for their decisions and predictions. In deep learning, where complex neural networks often operate as "black boxes", the importance of explainable AI lies in enhancing trust, accountability, and interoperability. For further advancement of explainable artificial intelligence in deep learning, gaining a deep understanding of its applications, approaches, evaluation metrics, current advancements, and challenges is imperative. Therefore, in this article, we began exploring the vast array of applications of explainable AI in different deep learning models, scrutinizing them within the context of existing research. We then explored explainable AI approaches used in Deep Learning models and discussed prevalent evaluation metrics used in evaluating a model's explainability. Subsequently, we precisely reviewed the experimental results and advancements of recent state-of-the-art experiments related to explainable AI in deep learning. Finally, we discussed the diverse challenges encountered in sentiment analysis and proposed future research directions to mitigate these concerns. This extensive review provides a complete understanding of explainable AI in deep learning, covering its applications, approaches, experimental analysis, challenges, and research directions. [ABSTRACT FROM AUTHOR]