Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles

• URL: http://arxiv.org/abs/2402.15572v1
• Date: Fri, 23 Feb 2024 19:14:57 GMT
• Title: Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles
• Authors: Shihong Ling, Yue Wan, Xiaowei Jia, Na Du
• Abstract summary: Recent explainable automated vehicles (AVs) neglect crucial information related to inherent uncertainties while providing explanations for actions. This study builds upon the "object-induced" model approach that prioritizes the role of objects in scenes for decision-making. We also explore several advanced training strategies guided by uncertainty, including uncertainty-guided data reweighting and augmentation.
• Score: 13.514721609660521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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