Survey on Vision-Based Risk Assessment for Automated Very Light Rail Transportation

In recent years, various Automated Driving Systems (ADS) leveraging machine learning (ML) and deep learning (DL) have been developed to enhance passenger comfort and safety. These systems can monitor and record accident-prone events, such as the behavior of road users and abnormal occurrences, using data from sensors placed at different locations on the vehicle. However, similar advancements are still in the early stages within the railway domain, where automotive applications must be adapted for trains, requiring appropriate sensor systems and vehicle-specific adjustments. This article reviews the primary approaches to vision-based live risk assessment (VLRA) for light rail or very light rail systems vehicles that bridge the gap between cars and trains, representing an intermediate stage in transportation technology. The survey categorizes the main approaches based on the type of risk factor, learning-based methods used, evaluation metrics, specific sensors or processes involved, and relevant datasets. Finally, we provide conclusions and propose directions for future research.