Safety Prediction Task

Safety prediction tasks aim to anticipate hazardous events, primarily focusing on autonomous systems like vehicles and robots. Current research emphasizes developing robust and efficient models, employing architectures such as transformers, Bayesian methods (like Deep Ensembles and MC-Dropout), and generative world models, often incorporating uncertainty quantification for improved reliability. This work is crucial for enhancing the safety and trustworthiness of autonomous systems, with applications ranging from autonomous driving to industrial safety, improving both system design and human-machine interaction.