A Review of Uncertainty Estimation Methods in Autonomous Driving Object Detection
With the advancement of autonomous driving technology,the accuracy and reliability of object detection have become increasingly crucial.Deep learning,as a core component of autonomous driving systems,significantly influences the safety and stability of these systems by estimating the uncertainty in predictive results.The paper summarizes the application of deep learning uncertainty estimation in autonomous driving object detection and discusses the significance of an effective uncertainty evaluation system.Firstly,the paper introduces the fundamental theories of deep learning uncertainty estimation,including Bayesian neural networks,Monte Carlo methods,and ensemble learning.These methods quantify model prediction uncertainty in different ways,providing autonomous driving systems with richer information.Secondly,the paper delves into the application of uncertainty estimation in autonomous driving object detection.Through case studies,it demonstrates how uncertainty information can be used to improve detection accuracy,especially in complex environments and extreme conditions.In these scenarios,uncertainty estimation provides decision support,helping the system avoid potential risks.Lastly,the paper summarizes the evaluation metrics for uncertainty estimation in autonomous driving object detection,considering both the model's predictive performance and the accuracy of the uncertainty estimation.
国家科技期刊平台
NSTL
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