自动驾驶目标检测不确定性估计方法综述

A Review of Uncertainty Estimation Methods in Autonomous Driving Object Detection

随着自动驾驶技术的发展,目标检测的准确性和可靠性变得至关重要。深度学习作为自动驾驶系统中的核心组成部分,其预测结果的不确定性估计对于系统的安全性和稳定性具有显著影响。总结了深度学习不确定性估计理论在自动驾驶目标检测中的应用,并探讨了有效的不确定性评价体系的重要性。介绍了深度学习不确定性估计的基本理论,包括贝叶斯神经网络、蒙特卡洛方法以及集成学习方法等。这些方法通过不同的途径量化模型预测的不确定性,为自动驾驶系统提供了更丰富的信息。深入探讨了自动驾驶目标检测中不确定性估计的应用。通过案例分析,展示了如何利用不确定性信息来提高目标检测的准确性,特别是在面对复杂环境和极端条件时,不确定性估计可以作为决策支持,帮助系统避免潜在的风险。总结了自动驾驶目标检测不确定性估计评价指标,同时,考虑了模型的预测性能、不确定性估计的准确性。

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.