This paper delves into the risks associated with overconfident AI models in autonomous driving systems. It discusses various safety mechanisms, such as reject classes, monitoring based on IF and LOF, uncertainty estimation methods, and more. The proposed diverse redundant safety mechanisms aim to improve decision-making processes and enhance system reliability.
The paper highlights the importance of detecting distribution shifts and adversarial perturbations in AI models. It emphasizes the need for a proactive approach to ensure the safety and dependability of AI algorithms in autonomous vehicles. The discussion includes detailed explanations of various error detection methods and their implementation details.
The study also introduces a voter system to improve the overall error detection method by integrating multiple redundant safety mechanisms. The 1oo3 and 2oo3 reliability checkers are compared based on their advantages and limitations. Future work involves investigating how these solutions can be applied to regression-based AI models and conducting experiments to evaluate their impact on performance metrics.
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by Mandar Pital... um arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.08208.pdfTiefere Fragen