  The rapid evolution of automated vehicles (AVs) has the potential to provide
safer, more efficient, and comfortable travel options. However, these systems
face challenges regarding reliability in complex driving scenarios. Recent
explainable AV architectures neglect crucial information related to inherent
uncertainties while providing explanations for actions. To overcome such
challenges, our study builds upon the "object-induced" model approach that
prioritizes the role of objects in scenes for decision-making and integrates
uncertainty assessment into the decision-making process using an evidential
deep learning paradigm with a Beta prior. Additionally, we explore several
advanced training strategies guided by uncertainty, including
uncertainty-guided data reweighting and augmentation. Leveraging the BDD-OIA
dataset, our findings underscore that the model, through these enhancements,
not only offers a clearer comprehension of AV decisions and their underlying
reasoning but also surpasses existing baselines across a broad range of
scenarios.
