智能网联车即插即用组网模型及决策融合算法

doi:10.3969/j.issn.1004-132X.2021.16.011

中国机械工程 ›› 2021, Vol. 32 ›› Issue (16): 1983-1993.DOI: 10.3969/j.issn.1004-132X.2021.16.011

智能网联车即插即用组网模型及决策融合算法

周晓春1;梁军1;陈龙1;王亚飞2;龚进峰3

1.江苏大学汽车工程研究院，镇江，212013
2.上海交通大学机械与动力工程学院，上海，200240
3.中国汽车技术研究中心汽车工程研究院，天津，300300

出版日期:2021-08-25 发布日期:2021-09-10
通讯作者: 梁军（通信作者），男，1976年生，教授、博士研究生导师。研究方向为智能交通、道路交通信息安全与控制、车辆主动安全理论及方法。E-mail：liangjun@ujs.edu.cn。
作者简介:周晓春，男，1995年生，硕士研究生。研究方向为智能交通与智能车辆。E-mail：771519152@qq.com。
基金资助:
国家重点研发计划(2017YFB0102503)；
国家自然科学基金(U1664258,51875255,61601203)；
江苏省高等学校自然科学研究重大项目(18KJA580002)

Intelligent Connected Vehicle PnP Networking Model and Decision Fusion Algorithm

ZHOU Xiaochun1;LIANG Jun1;CHEN Long1;WANG Yafei2;GONG Jinfeng3

1.School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang,Jiangsu,212013
2.School of Mechanical and Power Engineering,Shanghai Jiao Tong University,Shanghai,200240
3.China Automotive Technology Research Center Co.,Ltd.,Tianjin,300300

Online:2021-08-25 Published:2021-09-10

摘要/Abstract

摘要： 针对智能网联车（ICV）多传感器融合前组网流程繁琐的问题，提出了一种车载即插即用（PnP）环境感知传感器“自搜索(SS)自识别(SI)自标定（SC）”组网模型（简称“S-SIC”）。采用深度优先搜索规划算法自搜索PnP传感器并接入ADAS域；采用广播报文自识别PnP传感器并进行初始化；采用车体坐标系坐标转换矩阵及补偿算法自标定PnP传感器。针对ICV多传感器数据及特征级融合时设备难以兼容的问题，提出了K-m.AW决策级融合算法。实验结果表明：S-SIC模型搜索成功率达92%，搜索平均时间为1.79 s；在前车轨迹估计中，基于K-m.AW算法的多场景融合估计准确率较对比算法分别提高了7.6%、11.8%。

关键词: 智能网联车, 多传感器, 即插即用, 信息融合, 聚类

Abstract: To simplify the networking processes of ICVs before multi-sensor fusion, a networking model of self-search—self-identification—self-calibration(S-SIC) was proposed for a PnP environmental sensing sensor. The PnP sensors were searched and connected to ADAS domain by using the depth-first search planning algorithm. The PnP sensors were identified and initialized by using the broadcast messages. The PnP sensors were calibrated by the coordinate transformation matrix of the vehicle body coordinate system and compensation algorithm. Aiming at the incompatibility of ICV multi-sensor data-level and feature-level fusion, a K-m.AW decision-level fusion algorithm was proposed. The experimental results show that the S-SIC model search success rate reaches 92%, and the average search time is as 1.79 s. In the trajectory estimation of the preceding vehicle, the multi-scene fusion estimation based on K-m.AW algorithm is increased by 7.6% and 11.8% respectively compared with contrast algorithms.

Key words: , intelligent connected vehicle(ICV), multi-sensor, plug-and-play(PnP), information fusion, clustering

中图分类号:

U471.15

周晓春, 梁军, 陈龙, 王亚飞, 龚进峰. 智能网联车即插即用组网模型及决策融合算法[J]. 中国机械工程, 2021, 32(16): 1983-1993.

ZHOU Xiaochun, LIANG Jun, CHEN Long, WANG Yafei, GONG Jinfeng. Intelligent Connected Vehicle PnP Networking Model and Decision Fusion Algorithm[J]. China Mechanical Engineering, 2021, 32(16): 1983-1993.

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智能网联车即插即用组网模型及决策融合算法

Intelligent Connected Vehicle PnP Networking Model and Decision Fusion Algorithm

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