基于多传感器融合的智能汽车多目标检测技术综述

doi:10.3969/j.issn.1674-8484.2021.04. 002

汽车安全与节能学报 ›› 2021, Vol. 12 ›› Issue (4): 440-455.DOI: 10.3969/j.issn.1674-8484.2021.04. 002

基于多传感器融合的智能汽车多目标检测技术综述

王海¹(), 徐岩松¹, 蔡英凤²^,^*(), 陈龙²

1.江苏大学汽车与交通工程学院,镇江 212013,中国
2.江苏大学汽车工程研究院,镇江 212013,中国

收稿日期:2021-12-15 出版日期:2021-12-31 发布日期:2022-01-10
通讯作者: 蔡英凤
作者简介:* 蔡英凤(1985—）,女(汉),江苏,教授。E-mail: caicaixiao0304@126.com。
王海(1983—),男(汉),江苏,教授。E-mail: wanghai1019@163.com。
蔡英凤教授,工学博士,江苏大学汽车工程研究院院长,江苏省智能车辆及运行工程研究中心主任,博士生导师;入选教育部青年长江学者、中国科协青年人才托举工程;获江苏青年五四奖章、江苏省十大青年科技之星等荣誉。研究方向为智能网联汽车环境感知和动力学控制技术,主持包括国家自然科学基金重点项目在内的国家、省部级课题10余项,发表论文80余篇,出版专著2部,授权发明专利29项,科研成果获省部级一等奖3项、二等奖2项。
Prof. CAI Yingfeng, Ph. D, and the dean of Automotive Engineering Research Institute of Jiangsu University, the director of Jiangsu Intelligent Vehicle and Operation Engineering Research Center, a PhD supervisor. She was selected as a Young Changjiang Scholar of Ministry of Education, a member of Young Talents Entrusted Project of China Association for Science and Technology. She won the Jiangsu Youth May 4th Medal and the top ten youth science and technology stars of Jiangsu Province and other honors. Her research direction is intelligent connected vehicle environment perception and dynamics control technology. She has hosted more than 10 items of the national, provincial and ministerial research projects including key projects of the National Natural Science Foundation. She has published more than 80 papers and 2 monographs, and authorized invention patents 29 items. Her scientific researches have won 3 First Prizes and 2 Second Prizes at the provincial and ministerial level.
基金资助:
国家自然科学基金(U20A20333);国家自然科学基金(51875255);国家自然科学基金(U20A20331);国家自然科学基金(52072160);江苏省重点研发计划(BE2020083-3);江苏省重点研发计划(BE2019010-2);江苏省六大人才高峰项目(2018-TD-GDZB-022)

Overview of intelligent vehicle multi-target detection technology based on multi-sensor fusion

WANG Hai¹(), XU Yansong¹, CAI Yingfeng²^,^*(), CHEN Long²

1. Schoole of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China

Received:2021-12-15 Online:2021-12-31 Published:2022-01-10
Contact: CAI Yingfeng

摘要/Abstract

摘要：

随着人工智能领域的快速崛起,智能汽车环境感知技术也得到了突飞猛进的发展,智能汽车的感知系统离不开多个以及多种传感器之间的配合使用,基于多传感器融合的智能汽车多目标检测技术也成为当下的热门研究方向,工业界和学术界都对此提出了不同的解决方案。本文对驾驶环境下基于多传感器融合的多目标检测技术进行了概述和总结,介绍了常用的车载传感器、数据集以及多传感器融合的方法和分类,对最近的多传感器检测算法进行了梳理,最后对基于多传感器融合的智能汽车多目标检测技术进行了概述,并对此方向存在的挑战和未来的发展趋势进行了分析。

关键词: 智能汽车, 多传感器融合, 目标检测, 综述

Abstract:

With the rapid rise of the field of artificial intelligence, the environment sensing technology of intelligent vehicles has also developed by leaps and bounds. The sensing system of intelligent vehicles is inseparable from the cooperative use of multiple sensors, and the multi-target detection technology of intelligent vehicles based on multi-sensor fusion has also become a hot research direction. Industry and academia have proposed different solutions. This paper summarizes the multi-target detection technology based on multi-sensor fusion in intelligent vehicles, indroduces the commonly used on-board sensors, data sets, methods and classification of multi-sensor fusion, and the recent multi-sensor detection algorithms, and finally summarizes the multi-target detection technology of intelligent vehicles based on multi-sensor fusion, It also puts forward analysis on the challenges and future development trend in this direction.

Key words: intelligent vehicles, multi-sensor fusion, multi-target detection, overview

中图分类号:

TK411.+2

王海, 徐岩松, 蔡英凤, 陈龙. 基于多传感器融合的智能汽车多目标检测技术综述[J]. 汽车安全与节能学报, 2021, 12(4): 440-455.

WANG Hai, XU Yansong, CAI Yingfeng, CHEN Long. Overview of intelligent vehicle multi-target detection technology based on multi-sensor fusion[J]. Journal of Automotive Safety and Energy, 2021, 12(4): 440-455.

图/表 14

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分级	名称	持续的车辆横向和纵向运动控制	目标和时间探测与响应	动态驾驶任务后援	设计运行规范
0级	应急辅助	驾驶员	驾驶员及系统	驾驶员	有限制
1级	部分驾驶辅助	驾驶员和系统	驾驶员及系统	驾驶员	有限制
2级	组合驾驶辅助	系统	驾驶员及系统	驾驶员	有限制
3级	有条件自动驾驶	系统	系统	动态驾驶任务后援用户（执行接管后成为驾驶员）	有限制
4级	高度自动驾驶	系统	系统	系统	有限制
5级	完全自动驾驶	系统	系统	系统	无限制

分级	名称	持续的车辆横向和纵向运动控制	目标和时间探测与响应	动态驾驶任务后援	设计运行规范
0级	应急辅助	驾驶员	驾驶员及系统	驾驶员	有限制
1级	部分驾驶辅助	驾驶员和系统	驾驶员及系统	驾驶员	有限制
2级	组合驾驶辅助	系统	驾驶员及系统	驾驶员	有限制
3级	有条件自动驾驶	系统	系统	动态驾驶任务后援用户（执行接管后成为驾驶员）	有限制
4级	高度自动驾驶	系统	系统	系统	有限制
5级	完全自动驾驶	系统	系统	系统	无限制

传感器	优势	劣势	用途	成本
相机	分辨率高语义性强数据处理简单	雨雾天气效果差受光照条件影响容易产生虚警	障碍物检测交通信号灯检测交通标志检测车道线,人行横道检测	低
毫米波雷达	不受天气和光照影响测量范围较大	不适用于动态物体的检测易产生误检	障碍物检测测距测速	中
激光雷达	检测范围大检测精度高	成本高雨雾天气效果差	障碍物检测测距长短时记忆网络（LSTM）技术	高

传感器	优势	劣势	用途	成本
相机	分辨率高语义性强数据处理简单	雨雾天气效果差受光照条件影响容易产生虚警	障碍物检测交通信号灯检测交通标志检测车道线,人行横道检测	低
毫米波雷达	不受天气和光照影响测量范围较大	不适用于动态物体的检测易产生误检	障碍物检测测距测速	中
激光雷达	检测范围大检测精度高	成本高雨雾天气效果差	障碍物检测测距长短时记忆网络（LSTM）技术	高

数据集	Kitti	BDD	nuScenes	Waymo	ONCE
交通场景	城市郊区高速路	各种路况	城市	城市郊区	城市郊区
天气场景	白天晴天	晴天、多云、阴天、雨天、雪天、雾天	白天	白天、夜晚、黎明、黄昏、雨天,晴天	白天、夜晚晴天、多云、雨天
所用传感器	激光雷达灰度相机彩色相机 GPS	彩色相机 GPS IMU 陀螺仪	相机激光雷达彩色雷达 GPS IMU	激光雷达相机	激光雷达相机
提供的数据	约1.5万张图像点云数据 GPS和IMU数据	约10万段高清视频 10万张图像	约140万张图像点云数据	1 000段驾驶视频	约700万张图像点云数据
应用场景	立体视觉光流场景流 SLAM 物体检测与跟踪车道线检测语义分割	物体检测车道线检测驾驶区域检测语义分割	物体检测语义分割	物体检测与跟踪	物体检测
特点	目前最著名的自动驾驶数据集,提供多种优秀的基准	有各种注释的大规模车载数据集	注释多具有雷达	场景多	中国场景迄今最大的数据集

基于多传感器融合的智能汽车多目标检测技术综述

Overview of intelligent vehicle multi-target detection technology based on multi-sensor fusion

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[1]	赵树恩, 陈文斌, 邓召学, 刘伟. 基于扩张状态观测器的智能汽车弯道轨迹跟踪控制[J]. 汽车安全与节能学报, 2022, 13(1): 112-121.
[2]	武一民, 郑凯元, 高博麟, 陈明, 王义锋. 基于自适应扩展Kalman滤波的路侧多传感器融合[J]. 汽车安全与节能学报, 2021, 12(4): 522-527.
[3]	采国顺, 刘昊吉, 冯吉伟, 徐利伟, 殷国栋. 智能汽车的运动规划与控制研究综述[J]. 汽车安全与节能学报, 2021, 12(3): 279-297.
[4]	李亮, 王翔宇, 程硕, 陈翔, 黄超, 平先尧, 魏凌涛. 汽车底盘线控与动力学域控制技术[J]. 汽车安全与节能学报, 2020, 11(2): 143-160.
[5]	申恩恩，胡玉梅，陈光，罗攀，朱浩. 智能驾驶实时目标检测的深度卷积神经网络[J]. JASE, 2020, 11(1): 111-116.
[6]	李国法，陈耀昱，吕辰, 陶达，曹东璞，成波 . 智能汽车决策中的驾驶行为语义解析关键技术[J]. JASE, 2019, 10(4): 391-412.
[7]	胡远志，刘俊生，何佳，肖航，宋佳. 基于激光雷达点云与图像融合的车辆目标检测方法[J]. JASE, 2019, 10(4): 451-458.
[8]	金智林，何麟煊，赵万忠. 用于智能汽车的复杂光照环境车道线检测及跟踪方法[J]. JASE, 2019, 10(4): 459-466.
[9]	Gregory L. Plett. 锂离子电池荷电状态不同估算方法的综述及讨论（英文）[J]. JASE, 2019, 10(3): 249-272.
[10]	李升波, 关阳，侯廉，高洪波，段京良，梁爽，汪玉，成波，李克强，任伟，李骏. 深度神经网络的关键技术及其在自动驾驶领域的应用[J]. JASE, 2019, 10(2): 119-145.
[11]	荣芩，吴晓东，许敏. 基于ISO 标准的道路车辆线控转向系统的功能安全概念设计[J]. JASE, 2018, 9(3): 250-257.
[12]	业红玲，田英，张杰，等. 基于多模型切换的智能汽车横向控制[J]. JASE, 2018, 9(3): 288-294.
[13]	裴晓飞，刘昭度，马国成，叶阳. 汽车主动避撞系统的安全距离模型和目标检测算法[J]. 汽车安全与节能学报, 2012, 3(1): 26-33.