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Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles | IEEE Conference Publication | IEEE Xplore

Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles


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...Show More

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.CCS CONCEPTS• Computing methodologies → Neural networks; Supervised learning by classification; Object identification; • General and reference → Evaluation.
Date of Conference: 11-14 March 2024
Date Added to IEEE Xplore: 10 September 2024
ISBN Information:
Print on Demand(PoD) ISSN: 2167-2121
Conference Location: Boulder, CO, USA

References

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