汽车工程学报2024，Vol.14Issue(5)：760-771,12.DOI:10.3969/j.issn.2095‒1469.2024.05.02
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
摘要
Abstract
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.关键词
自动驾驶/目标检测/深度学习/不确定性估计Key words
autonomous driving/object recognition/deep learning/uncertainty estimation分类
信息技术与安全科学引用本文复制引用
赵洋,王潇,蔡柠泽,程洪..自动驾驶目标检测不确定性估计方法综述[J].汽车工程学报,2024,14(5):760-771,12.基金项目
国家重点研发计划项目(2022YFB2503004) （2022YFB2503004）
中央高校基本业务费项目(ZYGX2022J017) （ZYGX2022J017）
机器人与智能系统国际联合研究中心开放基金项目(JQZN2023-005) （JQZN2023-005）
