Systematic Review of the Automotive Testing Methodologies

This review is a testament to the dynamic nature of automotive Testing, tracing its evolution from classic testing processes to the game-changing integration of machine learning, deep learning, and reinforcement learning. The analysis unequivocally demonstrates that Artificial intelligence methodologies outperform traditional methods, particularly in groundbreaking technologies like autonomous driving functions. They excel in managing the complexity of vehicle systems, achieving comprehensive test coverage, and improving testing efficiency. However, challenges persist, including data privacy concerns, the intricacies of real-world scenario simulation, and the opacity of some AI models, highlighting the urgent need for continued research. The review underscores the potential of adaptive testing algorithms, cross-platform tools, and emerging technologies like GANs and transformer models. It emphasizes the ongoing necessity for research to address challenges and fully exploit AI’s capabilities in this rapidly evolving field, underlining the significance of each individual’s contribution to the future of automotive testing.