Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles
Authors: 
Shihong Ling, Yue Wan, Xiaowei Jia, Na DuAuthors Info & Claims
Shihong Ling, Yue Wan, Xiaowei Jia, Na DuAuthors Info & ClaimsPages 443 - 451
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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March 2024
982 pages
ISBN:9798400703225
DOI:10.1145/3610977
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Published: 11 March 2024
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