Safety Climate Perception

Safety climate perception research investigates how individuals perceive safety within various contexts, aiming to understand and improve safety practices in diverse settings, from autonomous vehicles to human-robot interaction and the trucking industry. Current research employs diverse methods, including neural networks (e.g., convolutional recurrent neural networks) for analyzing physiological and EEG data, deep learning models for predicting accidents, and clustering algorithms for identifying groups with distinct safety perceptions. These studies aim to enhance safety by improving the design of autonomous systems, optimizing human-robot interaction, and developing more effective safety interventions in high-risk industries, ultimately contributing to a safer and more efficient operational environment.