Autonomous driving is an application of engineering, data science, and computer science, besides other fields, presenting numerous design choices in system development. This review offers a structured timeline of the three fundamental types of autonomous driving: the traditional modular pipeline, the integrated end-to-end approach, and the recent surge in large transformer-based pre-trained models (including language, vision, multimodal, and vision-language domains). We detail the challenges and limitations that can be found in each methodology and how subsequent approaches have addressed these shortcomings. Furthermore, we provide in-depth analyses for examples of autonomous driving systems leveraging transformer architectures, which have demonstrated state-of-the-art performance and overcome the limitations of earlier methods. The paper concludes with a comparative study of these advanced models, a summary of the most frequently employed datasets and architectures, and a discussion of key trends in the field.