A comprehensive review of on-board action recognition models in public transportation systems
Cyril Meurie, Olivier Lézoray · Expert Systems with Applications · 2025
The emergence of autonomous transportation systems marks a significant milestone in modern mobility, promising enhanced safety, efficiency, and convenience. However, ensuring the safety of passengers remains a paramount concern in the development and deployment of such systems. Monitoring the interior of autonomous vehicles has emerged as a critical aspect to guarantee passenger safety, requiring robust on-board action recognition systems. This paper provides an overview of the challenges and advancements in on-board action recognition for interior monitoring in autonomous vehicles. A comprehensive review of datasets pertinent to interior monitoring is presented, encompassing diverse scenarios and conditions to facilitate the training and evaluation of on-board action recognition models. Furthermore, we explore the methodologies employed in the development of these systems, including traditional computer vision techniques, deep learning architectures, and multimodal approaches . By synthesizing insights from existing research and highlighting key challenges and advancements, this paper aims to contribute to the ongoing discourse on enhancing safety measures in future autonomous transportation systems (bus, metro , train, car) through effective interior monitoring and action recognition technologies.