Risk of Fully Autonomous Vehicle Operations, a Fault Tree Analysis

In recent years, Fully Autonomous Vehicles (FAVs) have attracted considerable attention in the transportation sector, promising enhanced safety and efficiency. However, pressing concerns regarding safety, cybersecurity, liability, infrastructure, and societal impact persist. While much research focuses on technical aspects, this study adopts a comprehensive approach through Fault Tree Analysis (FTA), examining broader risk factors such as component and infrastructure failures to provide valuable insights for FAV development and deployment. Findings indicate a higher failure probability of 0.2610 for component failure, while infrastructure failure has a probability of 1.470 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup>. The analysis highlights the significance of focusing on FAV components such as the light detection and ranging (LiDAR), camera and communication systems, with manufacturers and governments urged to establish robust regulations and quality standards in these areas to ensure FAV safety. Additionally, measures to address risks like reckless driving of conventional vehicle drivers, distractions, and extreme weather conditions are also crucial for promoting FAV safety.