Evaluation of Autonomous Vehicles Adoption Using the Technology Readiness Assessment Methodology: A Saudi Arabian Case Study

The transportation sector is a primary focus for Artificial Intelligence (AI) innovation because of ongoing technological progress. The sector faces growing challenges due to urbanization and congestion. Cities are considering electric and Autonomous Vehicles (AVs) to be promising future solutions because of their potential to enhance efficiency and reduce emissions, thereby helping us to achieve sustainability goals and drive the digital transformation. However, adopting these technologies requires evaluating their real-world deployment readiness. This research is motivated by the need to address this gap. The research develops and validates a smart framework through a technology readiness assessment methodology to evaluate AV readiness, combining rule-based reasoning with a weighted scoring and threshold-based classification model, along with Large Language Model (LLM) analysis. PESTEL and SWOT analyses were conducted to understand the current state of AV readiness in Saudi Arabia. The system was validated against a benchmark dataset derived from published sources of expert assessments across 20 countries. The system achieved a 97.1% agreement rate and a 0.44 Mean Absolute Error (MAE). The robustness of the weighted aggregation model was demonstrated through a Monte Carlo simulation, achieving a 92.1% stability rate when dealing with uncertain conditions. The framework proved its real-world value through a Saudi Arabian case study with an overall predicted TRL at 7–8, identifying social acceptance and regulation as a priority for development. These findings will advance AI adoption in the transport sector by providing a multidimensional framework to evaluate the readiness for deploying AVs.