摘要
多模态融合感知是自动驾驶的研究热点之一,然而在复杂交通环境下由于天气、光照等外部因素干扰,目标识别可能出现错误,融合时会不可避免地出现分类冲突问题。为此,本文提出一种基于D-S证据理论的多模态结果级融合框架,将深度神经网络的置信度得分输出并作为D-S证据理论的概率密度函数,通过证据组合修正冲突的分类结果,该框架可以解决任意模态之间融合的分类冲突问题。基于KITTI数据集对该框架进行实验验证,实验测试的结果表明,框架输出的融合结果较单一感知网络mAP值均能提高8%左右,其中Yolov3与Pointpillar的融合结果相较于Pointpillar单一网络感知结果mAP值提高32%,且在复杂交通环境下能够有效解决多模态融合后的分类冲突问题。
Multi-modal fusion perception is one of the research hotspots of automatic driving.However,in complex traffic environment,due to the interference of weather,illumination and other external factors,the target recognition may be wrong,leading to inevitable classification conflict during fusion.Therefore,this paper proposes a multi-modal late fusion framework based on D-S Evidence Theory.The confidence score of deep neural network is output and used as the probability density function of D-S evidence theory.By modifying the classification result of conflict through evidence combination,this framework can solve the classification conflict problem of fusion be‐tween any mode.The framework is verified by experiments based on KITTI data set.The results show that the fusion result of the framework output can increase by about 8%compared with the mAP value of a single sensing network,with the fusion result of Yolov3 and Pointpillar increasing by 32%compared with the single sensing result of Point‐pillar,which can effectively solve the classification conflict after multi-mode fusion in the complex traffic environ‐ment.
作者
程腾
侯登超
张强
石琴
郭利港
Cheng Teng;Hou Dengchao;Zhang Qiang;Shi Qin;Guo Ligang(School of Automotive and Transportation Engineering,Hefei University of Technology,Hefei 230000;Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province,Hefei 250000;Key Laboratory for Automated Vehicle Safety Technology of Anhui Province,Hefei 230009;Chery Automobile Co.,Ltd.,Wuhu 241000)
出处
《汽车工程》
EI
CSCD
北大核心
2023年第10期1815-1823,共9页
Automotive Engineering
基金
国家自然科学基金(82171012)
安徽省自然科学基金(2208085MF171)
中央高校基本科研业务费专项资金(JZ2023YQTD0073)
汽车标准化公益性开放课题资助项目(CATARC-Z-2022-01350)资助。
关键词
D-S证据理论
多模态融合
目标识别
分类冲突
D-S evidence theory
multimodal fusion
object recognition
conflict classification
