Pedestrian intention detection systems are essential for the safety of autonomous vehicles. This research explores the integration of uncertainty modeling into such systems to improve reliability in complex environments. The study addresses aleatory and epistemic uncertainties using machine learning techniques to enhance prediction robustness. The results show further possibilities for prediction accuracy and reliability, highlighting the importance of uncertainty-aware approaches insafety-critical applications.