Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles
This work addresses reliability challenges in automated vehicles by enhancing explainability with uncertainty, though it appears incremental as it builds on prior object-induced models.
The study tackled the problem of explainable automated vehicles by integrating uncertainty assessment into an object-induced model, resulting in improved comprehension and performance that surpassed existing baselines across various scenarios.
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.