The field of autonomous driving and eye tracking is moving towards enhanced robustness and reliability in adverse weather conditions and complex environments. Researchers are focusing on developing innovative approaches to improve the accuracy and calibration of predictive uncertainty estimates, which is crucial for safety-critical applications. Noteworthy papers in this regard include: Enhancing Self-Driving Segmentation in Adverse Weather Conditions, which introduces a dual uncertainty-aware training approach to improve segmentation robustness. Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation, which shows that mixtures of experts can yield more reliable uncertainty estimates than ensembles. Other notable works include the development of adaptive scoring frameworks for attention assessment in children with Neurodevelopmental Disorders and personalized inhibition training with eye-tracking for educational games.