汽车工程学报2024，Vol.14Issue(5)：772-780,9.DOI:10.3969/j.issn.2095‒1469.2024.05.03
面向自动驾驶感知的快速不确定性估计方法
A Fast Uncertainty Estimation Method for Autonomous Driving Perception
摘要
Abstract
In the visual perception task of autonomous driving,it is crucial to accurately and quickly extract the cognitive and accidental uncertainties to effectively resolve the Safety of the Intended Functionality(SOTIF)issues associated with autonomous driving.In traditional methods such as Monte Carlo dropout and deep ensembles,uncertainty is estimated by sampling the prediction results of different sub-models,which slows down the estimation and tends to occupy a large amount of memory in the processor during the model inference stage.A fast Monte Carlo dropout method and a technique for correcting subsequent detection results are proposed to address the issues of slow estimation of uncertainty in Monte Carlo dropout and the selection of subsequent detection results.This method uses a multi-head mechanism to replace the traditional multiple sampling mechanism in Monte Carlo dropout,thereby saving time in both sampling and inference throughout the uncertainty estimation process.关键词
自动驾驶/不确定性估计/目标检测/预期功能安全Key words
autonomous driving/uncertainty estimation/object detection/safety of the intended functionality分类
交通工程引用本文复制引用
王潇,赵洋,程洪..面向自动驾驶感知的快速不确定性估计方法[J].汽车工程学报,2024,14(5):772-780,9.基金项目
国家自然科学基金项目(U1964203) （U1964203）
国家重点研发计划项目(2022YFB2503004) （2022YFB2503004）
四川省重点研发项目(2022YFG0342) （2022YFG0342）
