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Article(id=1194640797623619863, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1194640796491162512, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20240105, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1762754161505, onlineDateStr=2025-11-10, pubDate=1743782400000, pubDateStr=2025-04-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1762754161505, onlineIssueDateStr=2025-11-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1762754161505, creator=13701087609, updateTime=1762754161505, updator=13701087609, issue=Issue{id=1194640796491162512, tenantId=1146029695717560320, journalId=1189645257101713411, year='2025', volume='', issue='4', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1762754161236, creator=13701087609, updateTime=1762754161236, updator=13701087609, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=12, endPage=22, ext={EN=ArticleExt(id=1194640797883666713, articleId=1194640797623619863, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Applications and Development Trends of Common Environmental Perception Sensors in the Field of Intelligent Driving, columnId=1194640797204194193, journalTitle=Automotive Digest, columnName=Special Issue on Reviews of Frontiers in Automotive Technologies by Fujian University of Technology, runingTitle=null, highlight=null, articleAbstract=

To meet the demand for precise environmental perception in intelligent driving, this paper focuses on the application and development of intelligent driving environment perception sensors, analyzing their foundational position in system architecture. It discusses the working mechanisms, application scenarios, and advantages and disadvantages of various sensors such as visual sensors, LiDAR, millimeter-wave radar, and ultrasonic radar, analyzing the impact of multi-sensor fusion on improving perception accuracy and autonomous driving reliability. In the future, environmental perception sensor technology for intelligent vehicles will focus on 6 major directions: new sensing materials, intelligent adaptability, energy-efficient design, fault diagnosis, real-time calibration, and environmental impact suppression. Sensors will continue to evolve towards miniaturization, integration, and adaptive low-power design. Breakthroughs will be made in areas such as high-precision vehicle cameras, solid-state LiDAR, and innovative integration of ultrasonic radar, aiming to enhance sensing accuracy, real-time performance, and reliability to meet the increasingly complex demands of driving environments.

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为满足智能驾驶对精准环境感知的需求，聚焦智能驾驶环境感知传感器的应用与发展，解析其在系统架构中的基础性地位。讨论了视觉传感器、激光雷达、毫米波雷达和超声波雷达等各类传感器的工作机制、应用场景以及优劣势，分析了多传感器融合对提高感知精度与智能驾驶可靠性的影响。未来，智能车辆的环境感知传感器技术将围绕新型传感材料、智能自适应、节能设计、故障诊断、实时校准和环境影响抑制6大方向发展；传感器将向小型化、集成化及自适应低功耗设计方面持续演进；将在车载摄像头高精度化、激光雷达固态化、超声波雷达融合创新等方面取得更大突破，以提升传感精确度、实时性和可靠性，满足日益复杂的驾驶环境需求。

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IEEE Access, 2022, 10: 14643-14668., articleTitle=On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1194655732730663099, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, xref=null, ext=[AuthorCompanyExt(id=1194655732739051708, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, companyId=1194655732730663099, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Fujian University of Technology, Fuzhou 350118), AuthorCompanyExt(id=1194655732747440317, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, companyId=1194655732730663099, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=福建理工大学， 福州 350118)])], figs=[ArticleFig(id=1194655734223835348, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=EN, label=null, caption=null, figureFileSmall=Q1m/Ejm6RirLABQsN6sWAQ==, figureFileBig=rx4riZDLl3csJRfWyFatDA==, tableContent=null), ArticleFig(id=1194655734290944213, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=CN, label=图1, caption=智能驾驶典型系统架构, figureFileSmall=Q1m/Ejm6RirLABQsN6sWAQ==, figureFileBig=rx4riZDLl3csJRfWyFatDA==, tableContent=null), ArticleFig(id=1194655734362247382, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=EN, label=null, caption=null, figureFileSmall=TX881+8HnpuezxPBt5k/Pg==, figureFileBig=4cQPuUdrytPLAIQhCs1eeA==, tableContent=null), ArticleFig(id=1194655734429356247, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=CN, label=图2, caption=智能驾驶基本技术架构, figureFileSmall=TX881+8HnpuezxPBt5k/Pg==, figureFileBig=4cQPuUdrytPLAIQhCs1eeA==, tableContent=null), ArticleFig(id=1194655734483882200, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
厂商 型号 像素/×106像素点 帧率/帧·s-1 光学格式/英寸 分辨率
Onsemi AR2020CSSM13SMTA0-DP 20.00 60 1/1.8
Onsemi AR0830CSSH11SMKA0-CP-E 8.30 60 1/3
Onsemi AR0823AT2C18XUEA0-DPBR-E 8.30 60 1/1.8
Onsemi AR0544CSSH11SMKA0-CP-E 5.00 60 1/4.2
OmniVision OX08B40 8.30 36 1/1.73 3 840×2 160
OmniVision OX08D10 8.00 45 1/1.73 1 920×1 080
OmniVision OX05B 5.00 60 1/2.53 2 592×1 944
Smartsens SC320AT 3.00 30 1/2.44 1 940×1 556
Smartsens SC533AT 5.00 60 1/2.53 2 592×1 944
Smartsens SC850AT 8.30 30 1/1.73 3 856×2 176
Samsung ISOCELL Auto 3B6 3.00 60 @HDR 1/2.4 1 920×1 536
Samsung ISOCELL Auto 1H1 8.30 36 @HDR 1/1.7 3 840×2 160
Samsung ISOCELL Auto 4AC 1.20 60 @HDR 1/3.7 1 280×960
Sony IMX728 8.39 1/1.72
Sony IMX623 3.00 1/2.42
Sony ISX021 2.51 1/2.57
Sony ISX020 1.23 1/3.75
Sony IMX424 7.42 1/1.7
Sony IMX490 5.40 1/1.55
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车载用图像传感器性能参数

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厂商 型号 像素/×106像素点 帧率/帧·s-1 光学格式/英寸 分辨率
Onsemi AR2020CSSM13SMTA0-DP 20.00 60 1/1.8
Onsemi AR0830CSSH11SMKA0-CP-E 8.30 60 1/3
Onsemi AR0823AT2C18XUEA0-DPBR-E 8.30 60 1/1.8
Onsemi AR0544CSSH11SMKA0-CP-E 5.00 60 1/4.2
OmniVision OX08B40 8.30 36 1/1.73 3 840×2 160
OmniVision OX08D10 8.00 45 1/1.73 1 920×1 080
OmniVision OX05B 5.00 60 1/2.53 2 592×1 944
Smartsens SC320AT 3.00 30 1/2.44 1 940×1 556
Smartsens SC533AT 5.00 60 1/2.53 2 592×1 944
Smartsens SC850AT 8.30 30 1/1.73 3 856×2 176
Samsung ISOCELL Auto 3B6 3.00 60 @HDR 1/2.4 1 920×1 536
Samsung ISOCELL Auto 1H1 8.30 36 @HDR 1/1.7 3 840×2 160
Samsung ISOCELL Auto 4AC 1.20 60 @HDR 1/3.7 1 280×960
Sony IMX728 8.39 1/1.72
Sony IMX623 3.00 1/2.42
Sony ISX021 2.51 1/2.57
Sony ISX020 1.23 1/3.75
Sony IMX424 7.42 1/1.7
Sony IMX490 5.40 1/1.55
), ArticleFig(id=1194655734701986010, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
视角 类型 数量/颗 功能
前视 单目/双目
/广角		 1~4 FCW，LDW，交通标志识别（Traffic Sign Recognition, TSR），自适应巡航控制（Adaptive Cruise Control, ACC），行人碰撞预警（Pedestrian Collision Warning, PCW），车道保持辅助（Lane Keeping Assist, LKA），全景影像系统（Around View Monitor, AVM）
后视 广角 1~4 环视泊车辅助（Surround View Parking, SVP）, LDW, 泊车辅助（Parking Assist, PA）, AVM
环视 广角/鱼眼 4~8 PA
侧视 广角 2 盲点检测系统（Blind Spot Detection, BSD），AVM
内置 广角 1 驾驶员监控系统（Driver Monitoring System, DMS）
), ArticleFig(id=1194655734769094875, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=CN, label=表2, caption=

车辆ADAS摄像头系统配置

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视角 类型 数量/颗 功能
前视 单目/双目
/广角		 1~4 FCW，LDW，交通标志识别（Traffic Sign Recognition, TSR），自适应巡航控制（Adaptive Cruise Control, ACC），行人碰撞预警（Pedestrian Collision Warning, PCW），车道保持辅助（Lane Keeping Assist, LKA），全景影像系统（Around View Monitor, AVM）
后视 广角 1~4 环视泊车辅助（Surround View Parking, SVP）, LDW, 泊车辅助（Parking Assist, PA）, AVM
环视 广角/鱼眼 4~8 PA
侧视 广角 2 盲点检测系统（Blind Spot Detection, BSD），AVM
内置 广角 1 驾驶员监控系统（Driver Monitoring System, DMS）
), ArticleFig(id=1194655734844592348, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
厂商 类型 型号 芯片 像素/×106像素点 动态范围/dB 水平视场角/(°) 垂直视场角/(°)
Bosch 前视/单目 MPC3 Infineon 2.6 110 ±50 +27/-21
Continental 前视/单目 MFC500 NVIDIA 8 125
Continental 环视 SVC300 1.3~3 >185
Magna Gen5 system EyeQ™ 5 8 120 48
ZF 前视/单目 Smart Camera 6 EyeQ™ 6 8 120
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车载摄像头模组核心参数对比

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厂商 类型 型号 芯片 像素/×106像素点 动态范围/dB 水平视场角/(°) 垂直视场角/(°)
Bosch 前视/单目 MPC3 Infineon 2.6 110 ±50 +27/-21
Continental 前视/单目 MFC500 NVIDIA 8 125
Continental 环视 SVC300 1.3~3 >185
Magna Gen5 system EyeQ™ 5 8 120 48
ZF 前视/单目 Smart Camera 6 EyeQ™ 6 8 120
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厂商 型号 点频/Mpts·s-1 探测距离/m@10% 视场角度（H×V）/(°) 角分辨率（H×V）/(°) 波长/nm
Innoviz Innoviz one 250 115×25 0.1×0.1 905
Innoviz Innoviz Two 300 120×43 0.05×0.05 905
Innoviz Innoviz 360 300 360×64 0.05×0.05
Velodyne Velarray H800 200 120×16 0.22×0.25/0.5 905
Velodyne Alpha Prime 4.8 300 360×40 0.2×0.1 903
Valeo SCALA Gen 2 0.260 8 150 133×10 0.125/0.25×0.6 905
Valeo SCALA Gen 3 12.5 190 120×26 0.05×0.05
Hesai AT128 1.536 200 120×25.4 0.1×0.2 905
Hesai AT512 12.288 300 120×25.6 0.05×0.05
Hesai Pandar128 3.456 200 360×40 0.1×0.125
Huawei - 250 120×25 0.25×0.26 905
Livox HAP 0.452 150 120×25 0.18×0.23 905
RoboSense M1 0.787 5 150 120×25 0.2×0.1~0.2×0.2 905
RoboSense M1 Plus 0.787 5 180 120×25 0.2×0.1~0.2×0.2 905
RoboSense M2 1.575 200 0.1×0.2
RoboSense M3 300 0.05×0.05 940
MicroVision MAVIN™ N >14 220 100×25 0.086×0.04 905
Ibeo NEXT 140 11.2×5.6 0.09×0.07 885
Luminar IRIS 200 120×28 0.06×0.06 1 550
Seyond Falcon 250 120×25 0.06×0.06 1 550
Seyond Robin 200 120×24 0.1×0.1 905
Isurestar CK-128 200 120×25.6 0.1×0.2 905
Isurestar C-Fans-256 0.64 200 150×30 0.09×0.12
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智能驾驶LiDAR关键性能参数对比

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厂商 型号 点频/Mpts·s-1 探测距离/m@10% 视场角度（H×V）/(°) 角分辨率（H×V）/(°) 波长/nm
Innoviz Innoviz one 250 115×25 0.1×0.1 905
Innoviz Innoviz Two 300 120×43 0.05×0.05 905
Innoviz Innoviz 360 300 360×64 0.05×0.05
Velodyne Velarray H800 200 120×16 0.22×0.25/0.5 905
Velodyne Alpha Prime 4.8 300 360×40 0.2×0.1 903
Valeo SCALA Gen 2 0.260 8 150 133×10 0.125/0.25×0.6 905
Valeo SCALA Gen 3 12.5 190 120×26 0.05×0.05
Hesai AT128 1.536 200 120×25.4 0.1×0.2 905
Hesai AT512 12.288 300 120×25.6 0.05×0.05
Hesai Pandar128 3.456 200 360×40 0.1×0.125
Huawei - 250 120×25 0.25×0.26 905
Livox HAP 0.452 150 120×25 0.18×0.23 905
RoboSense M1 0.787 5 150 120×25 0.2×0.1~0.2×0.2 905
RoboSense M1 Plus 0.787 5 180 120×25 0.2×0.1~0.2×0.2 905
RoboSense M2 1.575 200 0.1×0.2
RoboSense M3 300 0.05×0.05 940
MicroVision MAVIN™ N >14 220 100×25 0.086×0.04 905
Ibeo NEXT 140 11.2×5.6 0.09×0.07 885
Luminar IRIS 200 120×28 0.06×0.06 1 550
Seyond Falcon 250 120×25 0.06×0.06 1 550
Seyond Robin 200 120×24 0.1×0.1 905
Isurestar CK-128 200 120×25.6 0.1×0.2 905
Isurestar C-Fans-256 0.64 200 150×30 0.09×0.12
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厂商 型号 精度/cm 测量范围/m 视野（H×V）/(°) 频率/kHz
Bosch Ultrasonic Sensors 6.0 6 0.15~2.5
Bosch Ultrasonic Sensors 6.1 6 0.15~4.5
Bosch Ultrasonic Sensors 6.5 3 0.15~5.5
Valeo Ultrasonic Sensor System 0.15~4.1 75×45 51.2
Continental CUS320 0.16~5.5 120×60 52
Murata MA48CF15-7N 0.15~5.5 120×60 48.2±1.0
AUDIOWELL AK2 TK0158 2 0.15~7 90±10×45±10 55.5±1.0
), ArticleFig(id=1194655735192719585, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=CN, label=表5, caption=

车载超声波雷达性能参数对比

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厂商 型号 精度/cm 测量范围/m 视野（H×V）/(°) 频率/kHz
Bosch Ultrasonic Sensors 6.0 6 0.15~2.5
Bosch Ultrasonic Sensors 6.1 6 0.15~4.5
Bosch Ultrasonic Sensors 6.5 3 0.15~5.5
Valeo Ultrasonic Sensor System 0.15~4.1 75×45 51.2
Continental CUS320 0.16~5.5 120×60 52
Murata MA48CF15-7N 0.15~5.5 120×60 48.2±1.0
AUDIOWELL AK2 TK0158 2 0.15~7 90±10×45±10 55.5±1.0
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属性 24 GHz雷达 77 GHz雷达
探视距离 短/中 远
探测角度 较大 较小
覆盖距离 最远可达约120 m 最远可达约280 m
穿透能力 较弱，受天气影响较大 较强，受天气影响较小
体积 不一定大于77 GHz雷达 能实现小型化
识别精度 较低 较高
分辨率 近距离最高可达厘米级别 一般优于24 GHz雷达
应用场景 BSD, LKA, LCA, LDW ACC, FCW, AEB, ADS
市场价格 较低 较高
安装位置 前方、后方 前方、两侧
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24 GHz与77 GHz毫米波雷达关键性能参数对比

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属性 24 GHz雷达 77 GHz雷达
探视距离 短/中 远
探测角度 较大 较小
覆盖距离 最远可达约120 m 最远可达约280 m
穿透能力 较弱，受天气影响较大 较强，受天气影响较小
体积 不一定大于77 GHz雷达 能实现小型化
识别精度 较低 较高
分辨率 近距离最高可达厘米级别 一般优于24 GHz雷达
应用场景 BSD, LKA, LCA, LDW ACC, FCW, AEB, ADS
市场价格 较低 较高
安装位置 前方、后方 前方、两侧
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属性 传统毫米波雷达 4D毫米波雷达
纵向天线 无 有
天线数 少 多
通道配置 3T4R/2T4R等 12T16R/48T48R等
俯仰角度/(°) 无 <2
方位角分辨率/(°) 5~8 <1
精度/(°) 0.3 0.1
最远探测距离/m ≤200 ≤300
点云信息 有限返回 数十倍返回、高密度点云
静止识别 识别难度大 可适应复杂路况
横向移动障碍物检测 识别难度大 可适应复杂路况
相邻人/物区分 不可区分 可区分
高度识别 高出物体和地面车辆难以区分 可在150 m处区分地面物体和立交桥
隐藏车辆探测率/% 20 80
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4D毫米波雷达与传统毫米波雷达参数对比

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属性 传统毫米波雷达 4D毫米波雷达
纵向天线 无 有
天线数 少 多
通道配置 3T4R/2T4R等 12T16R/48T48R等
俯仰角度/(°) 无 <2
方位角分辨率/(°) 5~8 <1
精度/(°) 0.3 0.1
最远探测距离/m ≤200 ≤300
点云信息 有限返回 数十倍返回、高密度点云
静止识别 识别难度大 可适应复杂路况
横向移动障碍物检测 识别难度大 可适应复杂路况
相邻人/物区分 不可区分 可区分
高度识别 高出物体和地面车辆难以区分 可在150 m处区分地面物体和立交桥
隐藏车辆探测率/% 20 80
), ArticleFig(id=1194655735524069606, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
厂商 型号 芯片数
/片		 收发通道数
（T/R）		 虚拟通道数 水平视场角/(°) 垂直视场角/(°) 水平角分辨率/(°) 垂直角分辨率/(°) 探测距离/m 距离分辨率/cm 速度分辨率/m·s-1 帧率
/Hz
Continental ARS540 4 12/16 192 120 8 1 2.3 300 40 0.1 20
Bosch Gen 5 4 8/16 128 120 24 302
ZF Premium 4 12/16 192 120 350
Mobileye 1 48/48 2 304 0.5 2 150
Arbe Phoenix 1 48/48 2 304 100 30 1 2 300 7.5~60 0.1 30
Arbe Lynx 1 24/12 288 140 30 2.5 6.4 260 80 0.1 18~30
Oculii EAGLE 2 6/8 2 000 120 30 0.5 1 350
Oculii FALCON 1 3/4 240 120 30 2 5 200
Freetech FVR40 2 6/8 48 120 30 1 1 320 10 0.2
SenseTime STA77-8 4 12/16 192 1 350
SenseTime STA77-6 2 6/8 48 120 24 2 4 280 20 0.09
Huawei 12/24 288 120 30 1 2 300
), ArticleFig(id=1194655735591178471, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=CN, label=表8, caption=

各厂商4D毫米波雷达产品关键技术参数对比

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厂商 型号 芯片数
/片		 收发通道数
（T/R）		 虚拟通道数 水平视场角/(°) 垂直视场角/(°) 水平角分辨率/(°) 垂直角分辨率/(°) 探测距离/m 距离分辨率/cm 速度分辨率/m·s-1 帧率
/Hz
Continental ARS540 4 12/16 192 120 8 1 2.3 300 40 0.1 20
Bosch Gen 5 4 8/16 128 120 24 302
ZF Premium 4 12/16 192 120 350
Mobileye 1 48/48 2 304 0.5 2 150
Arbe Phoenix 1 48/48 2 304 100 30 1 2 300 7.5~60 0.1 30
Arbe Lynx 1 24/12 288 140 30 2.5 6.4 260 80 0.1 18~30
Oculii EAGLE 2 6/8 2 000 120 30 0.5 1 350
Oculii FALCON 1 3/4 240 120 30 2 5 200
Freetech FVR40 2 6/8 48 120 30 1 1 320 10 0.2
SenseTime STA77-8 4 12/16 192 1 350
SenseTime STA77-6 2 6/8 48 120 24 2 4 280 20 0.09
Huawei 12/24 288 120 30 1 2 300
), ArticleFig(id=1194655735658287336, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
属性 视觉传感器 激光雷达 超声波雷达 毫米波雷达
类别 图像传感器 光学雷达 声学雷达 无线电雷达
工作原理 采集图像进行视觉处理和深度计算 光脉冲测距 声脉冲测距 电波测距及速度检测
波长 可见光/红外 905 nm/1 550 nm 40 KHz: 8.5 mm
58 KHz: 5.9 mm		 1~10 mm
成本 中 高 低 中
信号处理复杂程度 高 高 低 低
探测距离/m 50~500 0~300 0~10 15~250
数据量/Mbps 500~3 500 20~100 <0.01 0.1~15
算法要求 很高 较高 很低 一般
探测精度 中至高 高至超高 中 高
恶劣天气性能 受影响大 受一定影响 影响小 适应性强
), ArticleFig(id=1194655735733784809, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=CN, label=表9, caption=

车载传感器技术特性对比

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属性 视觉传感器 激光雷达 超声波雷达 毫米波雷达
类别 图像传感器 光学雷达 声学雷达 无线电雷达
工作原理 采集图像进行视觉处理和深度计算 光脉冲测距 声脉冲测距 电波测距及速度检测
波长 可见光/红外 905 nm/1 550 nm 40 KHz: 8.5 mm
58 KHz: 5.9 mm		 1~10 mm
成本 中 高 低 中
信号处理复杂程度 高 高 低 低
探测距离/m 50~500 0~300 0~10 15~250
数据量/Mbps 500~3 500 20~100 <0.01 0.1~15
算法要求 很高 较高 很低 一般
探测精度 中至高 高至超高 中 高
恶劣天气性能 受影响大 受一定影响 影响小 适应性强
), ArticleFig(id=1194655735800893674, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
技术 传感器 应用
车道线识别 摄像头 LDW, LKA
行人检测 摄像头、毫米波雷达、红外摄像头（夜晚） PCW, AEB
车辆检测 摄像头、毫米波雷达 FCW, AEB, ACC
交通标识检测 摄像头 TSR, ISLI, TLR
车辆定位 GPS, DSRC V2N, TSP
V2X通信 DSRC, C-V2X V2V, V2I, V2P
后方障碍物检测 摄像头、毫米波雷达 BCA, APA, BSD
), ArticleFig(id=1194655735859613931, tenantId=1146029695717560320, journalId=1189645257101713411, articleId=1194640797623619863, language=CN, label=表10, caption=

智能驾驶核心功能与传感器技术应用对照

, figureFileSmall=null, figureFileBig=null, tableContent=
技术 传感器 应用
车道线识别 摄像头 LDW, LKA
行人检测 摄像头、毫米波雷达、红外摄像头（夜晚） PCW, AEB
车辆检测 摄像头、毫米波雷达 FCW, AEB, ACC
交通标识检测 摄像头 TSR, ISLI, TLR
车辆定位 GPS, DSRC V2N, TSP
V2X通信 DSRC, C-V2X V2V, V2I, V2P
后方障碍物检测 摄像头、毫米波雷达 BCA, APA, BSD
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汽车文摘 | 福建理工大学汽车前瞻技术综述论文专刊 2025, (4): 12-22
常见环境感知传感器在智能驾驶领域的应用与发展趋势
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李翊轩, 查云飞
作者信息
  • 福建理工大学， 福州 350118
Applications and Development Trends of Common Environmental Perception Sensors in the Field of Intelligent Driving
Yixuan Li, Yunfei Zha
Affiliations
  • Fujian University of Technology, Fuzhou 350118
出版时间: 2025-04-05 doi: 10.19822/j.cnki.1671-6329.20240105
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摘要
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为满足智能驾驶对精准环境感知的需求，聚焦智能驾驶环境感知传感器的应用与发展，解析其在系统架构中的基础性地位。讨论了视觉传感器、激光雷达、毫米波雷达和超声波雷达等各类传感器的工作机制、应用场景以及优劣势，分析了多传感器融合对提高感知精度与智能驾驶可靠性的影响。未来，智能车辆的环境感知传感器技术将围绕新型传感材料、智能自适应、节能设计、故障诊断、实时校准和环境影响抑制6大方向发展；传感器将向小型化、集成化及自适应低功耗设计方面持续演进；将在车载摄像头高精度化、激光雷达固态化、超声波雷达融合创新等方面取得更大突破，以提升传感精确度、实时性和可靠性，满足日益复杂的驾驶环境需求。

智能驾驶  /  环境感知传感器  /  摄像头  /  激光雷达  /  毫米波雷达  /  超声波雷达

To meet the demand for precise environmental perception in intelligent driving, this paper focuses on the application and development of intelligent driving environment perception sensors, analyzing their foundational position in system architecture. It discusses the working mechanisms, application scenarios, and advantages and disadvantages of various sensors such as visual sensors, LiDAR, millimeter-wave radar, and ultrasonic radar, analyzing the impact of multi-sensor fusion on improving perception accuracy and autonomous driving reliability. In the future, environmental perception sensor technology for intelligent vehicles will focus on 6 major directions: new sensing materials, intelligent adaptability, energy-efficient design, fault diagnosis, real-time calibration, and environmental impact suppression. Sensors will continue to evolve towards miniaturization, integration, and adaptive low-power design. Breakthroughs will be made in areas such as high-precision vehicle cameras, solid-state LiDAR, and innovative integration of ultrasonic radar, aiming to enhance sensing accuracy, real-time performance, and reliability to meet the increasingly complex demands of driving environments.

Intelligent driving  /  Environmental perception sensor  /  Camera  /  LiDAR  /  Millimeter-wave radar  /  Ultrasonic radar
李翊轩, 查云飞. 常见环境感知传感器在智能驾驶领域的应用与发展趋势. 汽车文摘, 2025 , (4) : 12 -22 . DOI: 10.19822/j.cnki.1671-6329.20240105
Yixuan Li, Yunfei Zha. Applications and Development Trends of Common Environmental Perception Sensors in the Field of Intelligent Driving[J]. Automotive Digest, 2025 , (4) : 12 -22 . DOI: 10.19822/j.cnki.1671-6329.20240105
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0 引言
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在全球汽车产业变革中，交通压力与道路安全挑战凸显，智能驾驶技术迅速发展，成为行业创新焦点。传感器技术作为智能驾驶系统的核心支撑，是车辆的“感官中枢”，对于车辆自主导航、环境感知及精密决策执行等关键环节起着决定性作用[1]。其进步直接影响着车辆安全性能提升，并加速无人驾驶技术的商用化进程[2]。尤其在人工智能、大数据技术的强力驱动下，传感器技术发展迅速，为智能驾驶的落地实施奠定了坚实的基础。本文聚焦传感器技术在智能驾驶领域的应用与演进，阐述其基础支撑特性和最新发展，包括单车传感器与传感器融合技术等核心内容。深入剖析各类传感器在智能驾驶中的应用场景、研究进展，并预见未来发展趋势。最后，提出传感技术在智能驾驶领域的发展策略建议，旨在助力传感技术的创新迭代与广泛应用。
1 智能驾驶技术概述
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1.1 基本架构与关键技术构成
智能驾驶系统融合了多项先进技术，构建了环境感知、决策规划、控制执行3大关键模块，如图1所示[3]。环境感知模块利用视觉传感器、激光雷达（Light Detection and Ranging, LiDAR）、超声波雷达等设备实时收集道路信息、交通标志、行人与其他车辆信息，形成全面准确的环境模型[4-5]。决策规划模块扮演“大脑”角色，对接收到的感知数据进行深度学习与智能算法处理，制定适应复杂路况的最优行驶方案[6]。而控制执行模块则如同“手脚”，依据规划指令精确操控车辆动力、制动与转向系统，确保汽车在行驶时既遵守交规又能灵活适应环境变化，始终保持安全、稳定和可控的状态，从而实现从起点到终点的全自动、高效率驾驶。
1.2 传感器技术的基础性地位
智能驾驶技术架构核心由车载系统与云端系统构成，如图2所示。车载系统借助多元传感器如视觉传感器、雷达等，实时捕获环境和车辆状态数据，通过融合算法形成精确环境模型以支持决策控制。云端系统则强化模型学习与优化，提供高精度地图等增值服务。传感器的性能表现对于整个智能驾驶系统的可靠性和安全性发挥决定性影响[7]，尤其在实现L4及以上级别自动驾驶时，必须满足更高标准的探测精度、视野范围、全天候稳定性和复杂环境适应性要求。
2 常见传感器技术在智能驾驶中的应用
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2.1 各类传感器技术
智能驾驶系统中的环境感知模块主要依赖于多元传感器集成技术，其中包括2类核心关键传感器：光学传感器，例如视觉传感器和激光雷达，负责捕捉和解析视觉图像信息；非光学传感器，如毫米波雷达和超声波雷达，用于探测距离和速度等非视觉环境数据。这两类传感器协同工作，共同构建车辆对外部环境的精确、全面认知。
2.1.1 视觉传感器
表1列举了多种车载图像传感器产品，显现了5项技术发展特点：（1）高像素化趋势，5 MP以上产品占主流，甚至最高达到20 MP；（2）高速帧率受重视，多产品实现60帧/s、并能在高动态范围（High Dynamic Range, HDR）模式下保持高性能，满足动态场景需求；（3）大光学格式与多样化分辨率渐成标配，包括适应低光环境的大尺寸传感器及针对特定场景的非标准分辨率；（4）高分辨率趋势明显，如OmniVision与Samsung等品牌的部分产品已实现4 K分辨率；（5）HDR功能受到关注，Samsung ISOCELL系列等新品在高帧率下仍保持HDR性能，显示出HDR技术对提升复杂光线环境下图像质量的重要性。图像传感器技术正在向高像素、高帧率、小型化，以及4 K分辨率和HDR功能强化普及，以满足智能驾驶对高清、高速、宽视角的需求。
车载摄像头在智能驾驶系统中按安装位置和功能划分为前视、环视、后视等多种类型，并依据摄像头数量设计为单目、双目或多目系统[1]。前视摄像头对于前方碰撞预警（Forward Collision Warning, FCW）、车道偏离预警（Lane Departure Warning, LDW）等高级辅助驾驶功能的实现至关重要，尽管成本较高，已有8百万像素高性能产品投入使用，并预计未来可能增加前视摄像头数量以提升前方道路状况识别精度[8]。在L3级以下智能驾驶中，单目摄像头因性价比优势被广泛应用，但其在距离测量准确性和视角覆盖范围上存在局限性。随着智能驾驶功能需求不断拓展，多摄像头系统逐渐成为主流，以增强环境感知能力，尤其是在高级驾驶辅助系统（Advanced Driver Assistance Systems, ADAS）中，整合多种摄像头实现倒车影像、全景影像等功能[9]。
车载摄像头需严格遵照严苛工业标准，确保极端条件稳定耐用，根据现行标准QC/T 1128—2019《汽车用摄像头》要求[10]，其寿命需达8~10年。像素通常在5×106~13×106像素点，功率限制在10 W内；后视广角超135°，环视拼接可达100°~170°全景，前视视场40°~70°或更广至150°，探测距离远至250 m以上。
为了构建全方位、多维度的智能驾驶辅助系统，各类车辆已经广泛采用了不同类型的摄像头。表2列举了不同安装部位所配备的摄像头类型及其数量范围，以及各自对应的核心功能。
主要汽车零部件企业前视单目及环视摄像头系统如表3所示。车载摄像头系统主要呈现4方面改进：（1）像素提升至8×106像素点，以Infineon和NVIDIA芯片为核心实现高清成像；（2）单目摄像头如Bosch MPC3通过增强动态范围，以保证在复杂光照下保持良好图像质量；（3）视场角设计持续拓宽，如Continental SVC300提供的超185°水平视场角，确保车辆周围环境全面覆盖；（4）集成高性能AI处理器，如EyeQ™ 5和EyeQ™ 6，针对机器视觉与深度学习优化，以实现图像捕获至复杂实时分析的全程智能化，以满足智能驾驶对环境感知日益增强的需求。
2.1.2 激光雷达
LiDAR是基于激光测距与光探测技术，精密获取目标三维空间的信息。其中，单线束LiDAR仅产生单一扫描线，形成2D点云数据；多线束LiDAR则通过发射及接收多个激光脉冲同时扫描，生成更为详尽的2.5D或3D点云模型[11]，其线束数量从早期的4/8线束、16/32线束已经提升至40/64线束甚至192线束及以上，线束数目的增多直接提升了空间分辨率和测距精度[12]。
LiDAR系统的核心模块由4部分构成：（1）激光器发射脉冲，按波长类型可分为905 nm和1 550 nm，其中905 nm激光器因成本优势在技术转化方面更为领先。（2）扫描光学组件负责空间定向扫描[13]。（3）光电探测器与接收集成电路捕获并转换回波信号。（4）位置导航器件提供雷达自身定位信息[14]。
自西奥多·梅曼发明激光器以来[15]，LiDAR技术历经发展，已在航空航天、测绘等诸多领域得到了广泛应用[16]，而随着汽车智能化程度的提升，特别是在L3及以上级别的智能驾驶系统中，因其能够实现远距离、高精度、实时3D环境建模的独特优势而成为关键技术[17]。其不受光照条件限制的特性使得智能驾驶车辆能在各种环境下准确识别道路状况、障碍物以及其他重要交通标志[11]。
LiDAR利用不同的技术架构实现全景扫描，其中机械旋转式依赖机械旋转组件，半固态式结合固定收发模块与有限运动扫描元件以实现定向视角扫描，而固态式则完全基于固态电子机制消除机械运动，实现高速、高精度和小型化的扫描。半固态激光雷达因其平衡的实用性和技术成熟度，被广泛用于车辆智能驾驶感知领域。
固态化是LiDAR技术研发的核心方向，旨在通过创新固态扫描技术和阵列配置满足多样化需求，提升系统可靠性和紧凑性，同时降低成本[18]。随着技术的不断进步，LiDAR系统正在经历一场从传统的机械式结构向半固态乃至全固态设计的演进过程[19]。这一转变极大地推进了LiDAR的小型化、低功耗和高度集成化发展，从而有力地促进了其在智能驾驶和其他尖端领域的广泛应用。
表4汇总了多家LiDAR厂商如Innoviz、Velodyne等旗下多种型号产品的核心性能指标，可以看出LiDAR的最新进展主要表现在5个方面：（1）系统探测距离不断延伸，部分设备最远探测距离已达300 m，增强了早期环境感知，缩短了安全响应时间；（2）产品趋向于提供更大视场角度，包括实现360°全方位覆盖，以全面感知周围环境；（3）角分辨率持续优化，0.05°×0.05°的高精度成为现实，有力推动了三维空间建模的精确度和目标识别能力的提升；（4）激光点频不断提升，如Valeo SCALA Gen 3、Hesai AT512、MicroVision MAVIN™ N等，提高了实时捕获动态环境信息的能力；（5）虽然905 nm波长仍为主流，但部分产品采用1 550 nm等较长波长以优化穿透性能和适应各种天气条件下的稳定探测。
深度学习技术与LiDAR系统的集成优化了三维感知性能，在微机电系统激光雷达（MEMS LiDAR）中增强了视场覆盖和探测距离[20]，有效应对复杂环境下的高噪、非结构化3D点云数据挑战[21]，通过挖掘时间冗余信息提升处理效率而不损失精度[22]，对智能驾驶环境感知能力的提升具有显著意义，为LiDAR技术未来的发展提供了重要参考。
在智能驾驶的应用中，确保有效检测低反射率物体是关键技术挑战之一，现有幅度调制连续波（Amplitude Modulated Continuous Wave, AMCW）和频率调制连续波（Frequency Modulated Continuous Wave, FMCW）激光雷达技术受限[23]，普遍难以突破300 m的探测范围。而单光子激光雷达（Single-Photon LiDAR, SPL）的发展为解决这一距离探测瓶颈提供了新的可能性。SPL利用单个光子飞行时间测量实现高精度深度映射，尤其适用于低光照环境，具有远至数公里的探测范围和厘米级分辨率[24]，适合智能驾驶中的环境建模[25]。相较于传统的多光子检测LiDAR，SPL采用非线性探测技术，提升数据采集效率并增强物体分类准确性，尤其是在不同光照条件下。然而，SPL在面对低强度反射、高入射角及复杂环境如植被时，精度和多重返回解析能力受限[26]。尽管面临技术和成本挑战，单光子LiDAR在提升智能驾驶环境感知方面的潜力突出，尤其是在整合其他传感器如RGB相机的过程中，解决数据融合和校准问题有望进一步拓宽其应用场景[27]。
2.1.3 超声波雷达
超声波雷达利用超声波高频振动（>20 kHz）在介质中的传播与反射原理进行测距[28]。系统核心包含超声波发射器、接收器及信号处理单元，常见工作频率如40 kHz，以其适中的角度覆盖和有效探测范围而被广泛采纳[29]。通过发射超声波脉冲并精确测量回波返回时间，实现对目标距离的计算。此种技术特别适应于0.1~5 m的短距离应用场景，尤其在低速环境如泊车辅助中展现高精度优势[30]，但受限于超声波在远距离和高速条件下的衰减与延迟效应，不适用于高速远程距离测量场合[31]。
超声波雷达按信号类型和技术架构可分为模拟式、二线数位、三线主动数位及四线数位四种[32]。其中，模拟式雷达凭借成本优势广泛应用，但其抗干扰能力有限。二线数位雷达保持较低成本，但同样存在抗扰度不足的问题。三线主动数位雷达提升了信号辨识度，适合精密研究，但成本较高。四线数位雷达通过全数字化提高了抗干扰性能，虽然装配复杂度增加，但随着汽车智能化技术的发展，其在前瞻研究中占据显著地位。
车载超声波雷达主要分为超声波泊车辅助（Ultra-sonic Parking Assist, UPA）雷达和自动泊车辅助（Automatic Parking Assist, APA）雷达2类[33]。UPA雷达安装于汽车前后保险杠，主要用于实时探测15~250 cm范围内前后方向的障碍物，尤其在低速行驶、倒车或停车时提供安全保障，已广泛应用在各类车型尤其是倒车雷达系统中，常见配置为4个。APA雷达则位于汽车两侧，主要功能是测量停车位长度及泊车过程中的车辆位置，探测距离通常为30~500 cm，常用于高级自动泊车系统，典型配置含8个UPA雷达和4个APA雷达，负责车位识别和精准泊车指导[29]。
除此之外，凭借超声波雷达技术的低能耗、强穿透性、快速响应及环境独立性等优势，成为智能驾驶中如AEB、FCW、BSD、LCA及后方横向来车预警（Rear Cross Traffic Alert, RCTA）等多类主动安全系统不可或缺的核心传感技术，有效助力车辆实时探测周边环境以避免碰撞事故的发生。
不同制造商所生产的多种超声波雷达产品的详细规格如表5所示，其进步体现在精度与测量范围的提升上，如Bosch Ultrasonic Sensors 6.5传感器提高了短距离测量精度至3 cm，并将探测范围拓展至5.5 m，顺应了对更精准近距离及远距离探测的需求。多品牌产品视角宽度可达75°×45°至120°×60°，强调全面监测功能，而主流测量范围集中于0.15~5.5 m，反映出该区间在汽车倒车雷达等应用中的标准配置。工作频率普遍在50 kHz左右，体现该频段在确保测量性能与环境适应性间的良好平衡。未来，超声波雷达将实现更高精度、更大探测范围、更宽视野角以及更高的集成度与小型化设计，且会加强与其他传感技术融合，以满足智能驾驶对环境感知技术的严苛要求[33]。
2.1.4 毫米波雷达
毫米波雷达是一种精密雷达系统，专为24 GHz至300 GHz频段设计，利用此频谱区间内的电磁波进行探测。其波长介于微波与厘米波之间，兼顾了微波的穿透能力和光电雷达的高分辨率特点，尤其体现在小巧体积、高集成度和出色的测距、测速以及角度定位能力上。系统核心组件包括雷达罩、分体式机体、天线印刷电路板、高性能处理器、毫米波集成电路芯片以及电源管理单元[34]。
毫米波雷达系统采用FMCW调制技术，通过定向天线发送射频脉冲至目标区域。遇到障碍物后，电磁波会反射回来，雷达精确接收并分析回波的时间、频率特征以确定目标距离，利用多普勒效应获取目标的径向速度信息。此外，通过天线阵列技术和信号相位比较，能够精准测定目标在三维空间中的方位角。
毫米波雷达最初服务于军事用途，但随着技术进步，已成功跨界进入汽车行业的ADAS和其他民用领域，如智能驾驶车辆的距离和速度感知、目标识别及环境监测等，展现出在复杂气候和光照条件下优异的全天候工作能力，有效补充了激光雷达在某些特定环境下的局限性[35]。
车载毫米波雷达在ADAS中起关键作用，依据探测距离的不同主要分为短程、中程和长程3类[36]；按照工作频段划分，可以分为24 GHz和77 GHz两类。如表6所示，24 GHz雷达适用于近距离到中距离探测，常用于基础ADAS功能如BSD、LKA和LCA。相反，77 GHz雷达因具备更高分辨率、更远探测距离及更强穿透性[37]，使其在高速公路上的应用更为可靠[38]，适用于ACC、FCW、AEB等高级ADAS功能，在提升道路行驶安全方面发挥着不可或缺的作用。
鉴于24.25 GHz至27.5 GHz频段在全球范围内被分配给5G毫米波服务，各国开始推动车载雷达向更高频段迁移[39]。由此，24 GHz雷达在角雷达领域的应用将逐步被77 GHz雷达取代，后者所处的77~81 GHz频段被规划专用于汽车雷达。此外，77 GHz雷达因其小型化结构和卓越性能日益成为车载雷达的标准配置，标志着其在未来2至3年内将引领车载雷达技术的发展趋势。
传统车载毫米波雷达因测高能力有限，在智能驾驶应用中表现尚有不足，而激光雷达虽性能卓越但因成本问题尚未完全普及。鉴于此，4D毫米波雷达通过引入精确俯仰角感知并提高点云密度，实现了对距离、速度、角度及高度的高精度测量，且能生成类似激光雷达的高分辨率图像[8]，相比于传统3D毫米波雷达，其清晰程度可以提高5至10倍[12]。此外，4D毫米波雷达能在各种气候条件下提供卓越的物体检测性能，尤其在复杂场景如行人检测与隧道尺寸测量中表现出色[40]，增强了智能驾驶环境感知的安全性。
通过表7可知，4D毫米波雷达通过引入纵向天线设计和大规模天线阵列技术，实现了从二维到三维的高精度探测升级，显著提高了对复杂交通环境中各类目标的识别能力，还增加了探测距离和点云信息丰富度，进而强化了对远程、静止及横向移动目标的有效识别，并能精确测量高度信息以有效区分多层和遮挡目标，有力地增强了智能驾驶系统的态势感知性能。
尽管4D毫米波雷达理论上具有明显优势，但在实际应用中尚存挑战，如噪声抑制与点云密度优化间的权衡问题[41]，以及雷达数据集规模对深度学习算法发展的制约[42]。当前研究着重于提升雷达分辨率、降噪技术和改进数据处理算法，如使用随机稀疏步进频率波形（Random Sparse Step-Frequency Waveform, RSSFW）和多输入多输出（Multiple-Input Multiple-Output, MIMO）技术来增强雷达的空间分辨率[43]，并结合深度学习方法优化复杂路况下的探测性能[44-45]，探索新的算法来生成更高分辨率的点云图像从而提高其成像性能[46]。
表8列举了部分厂商的主要4D毫米波雷达产品的相关参数，可以看出当前4D毫米波雷达的核心发展趋势体现在3方面：首先，提升检测精度与分辨率是关键，通过增加通道数，例如Arbe Phoenix和Mobileye的产品拥有48T/48R甚至更高的配置，实现了对目标更为精细的识别能力。其次，雷达系统设计倾向于扩大视角并增强角分辨率，普遍产品水平视场角可达120°，从而拓宽探测范围并精准定位目标物体。再者，探测距离显著延长且速度与距离分辨率优化显著，诸如Arbe Lynx的高角度分辨率产品以及Continental ARS540、Huawei和SenseTime的部分产品能够实现300 m以上的远距离精确追踪。总体而言，4D毫米波雷达正持续向更高集成度、更高性能、更高分辨率以及更强环境适应性演进，以满足智能驾驶对复杂环境感知不断提升的标准要求。
2.2 传感器技术优劣势分析
智能驾驶感知系统中的车载摄像头、激光雷达、超声波雷达和毫米波雷达各具特色，应用场景各异并互为补充。通过表9可知，车载摄像头擅长视觉识别，适于交通标志、车道线和参与者识别，但受光照[47]、天气影响大[48]。激光雷达以高精度三维探测著称，尤其在障碍物识别与精密测距上有显著优势，虽成本高且易受极端天气干扰，仍是智能驾驶核心传感器。超声波雷达经济实用，擅长短程探测，如PA，不受光照影响但探测距离有限。毫米波雷达兼具中远距离探测与高分辨率测速测角能力，且对恶劣天气适应性强，常用于ACC、FCW等，随技术进步成本降低，精度提升。4种传感器应合理结合各自优势，共同赋能智能驾驶车辆在多种复杂环境中精准感知与决策。
3 传感器融合技术
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L4与L5级别的智能驾驶对环境感知的精密性和完整性有极高的要求，这促使了多传感器融合（Multi-Sensor Fusion, MSF）技术的应用。该技术的核心在于整合各类传感器的优势，如摄像头的视觉识别能力、毫米波雷达的距离和速度探测、激光雷达的三维环境建模及卫星导航提供的全局定位等功能，以此共同克服单一传感器在探测距离、视场角覆盖、抗干扰性以及复杂环境适应性等方面的局限性。如表10所示，MSF技术驱动下的多元化驾驶辅助功能，如LDW、LKA、FCW等，实现了行车全程智能化管控，并结合GPS、DSRC及V2X通信技术实现实时精确定位、通信及V2V、V2I信息共享[49]，以提升交通效率与安全性能。
MSF通过复杂的多传感器数据融合算法，可在数据处理顺序上分为早、中和后多层次融合[29,50,51]，有效提升障碍物识别、轨迹预测及路况理解的精度和可靠性[52]，从而增强智能驾驶的安全性。空间维度上的融合策略则优化组合各传感器优势，确保对复杂驾驶环境的全面适应。当前，前融合技术因其能简化感知架构、降低延迟和提高稳健性而成为发展趋势，但同时也对软件算法、芯片算力和数据通信提出了更高要求，目前正处于技术演进初期阶段。
技术上，卡尔曼滤波（Kalman Filter, KF）作为一种核心的数据融合技术，特别擅长在噪声环境中进行信息优化与状态估计，即便在数据质量不佳的情况下也能保证较高性能。针对非线性问题，通过扩展卡尔曼滤波（Extended Kalman Filter, EKF）等算法进行了适应性改进。
此外，融合系统进一步融入深度学习、神经网络等先进技术，结合概率统计理论如贝叶斯推理框架，以应对数据同步问题、优化融合算法并平衡计算资源[12]，目标是构造出既精确又稳定的多传感器融合感知系统，以支撑L4及L5级智能驾驶功能的有效部署和实施。
在智能驾驶感知方案中，一种是特斯拉为首的以摄像头为主导的视觉派方案，另一种则是大部分国内汽车厂商所强调的MSF以提高系统冗余度的方案。如小鹏汽车的Xpilot系统便采用视觉核心策略，并融合激光雷达以实现安全冗余。在硬件架构上，小鹏逐步构建了包含多种雷达、摄像头及高精定位在内的融合感知系统，并通过自主研发的XNet技术，将多摄像头数据转化为鸟瞰视图（Bird’s Eye View, BEV）视角信息，以此减少对高精地图的依赖，增强环境感知和决策能力。
4 总结与展望
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4.1 车载摄像头的技术革新
车载摄像头系统要实现L3及以上级别的智能驾驶，需要在硬件层面上实现镜头、CMOS传感器的小型化、低成本与国产化，同时依赖高效能计算平台支持的软件算法，实时精确处理和融合多摄像头数据以消除冗余信息，提升环境适应性和图像识别精度，确保智能驾驶的安全高效运行。其未来的发展目标将更侧重于适应多元驾驶场景，注重成像品质、成本效益与计算资源的有效分配，而非单纯追求高像素。关键技术革新主要体现在图像传感器技术、镜头材质与镜片制造工艺等方面，这些均是构建满足高级智能驾驶标准摄像头系统的核心基石。
4.2 车载激光雷达的固态化演进
车载激光雷达技术正朝固态化发展，以突破功率密度与探测距离局限，但小型化、低成本的高级固态雷达仍面临诸多挑战。当前研究聚焦硬件改进、振镜优化、扫描器升级和算法更新，尤其是拥有远距离探测优势的FMCW测距技术，但其在极端环境稳定性及高效数据处理上存在问题尚待解决。与此同时，固态雷达如光学相控阵（Optical Phased Array, OPA）和Flash技术虽展现出巨大潜力，但仍需突破旁瓣效应、工艺难度与成本控制等技术难关。此外，车载雷达数据处理算法急需增强复杂环境下的实时准确识别能力，为此需要构建一套统一标准体系，以推进算法的普适性、移植性和适应性，满足智能驾驶对高精度环境感知的严苛需求。
4.3 车载雷达的性能升级
超声波雷达相较于毫米波雷达和激光雷达，在长距离测量和分辨率方面存在性能局限，主要适用于短程探测和泊车辅助场景。尽管如此，提升其远距离精度、环境适应性及与其他传感器融合仍是技术研发的重要方向，同时也需追求成本效益及硬件的小型化与智能化。而毫米波雷达作为智能驾驶中实现高精度环境感知的核心元件，未来重点突破方向为高频信号处理，特别是在76~81 GHz频段，优化天线阵列以提高分辨率，以及保证射频组件与电路的高度集成，确保恶劣环境下精确的目标信息感知。随着4D成像雷达技术的演进，雷达系统对后端处理芯片的算力需求显著增加，这将促使雷达微控制器的角色和性能要求发生转变。同时，面对复杂设计挑战，研究还需着重解决大规模天线集成引发的空间干扰抑制问题，以及探索创新的信号处理算法应用。
4.4 多模态传感器融合技术的发展
智能驾驶技术的发展催生了对小型化、集成化传感器的需求，以整合多元传感数据以适应成本和空间限制，并提高环境感知精度。当前核心技术挑战在于如何实时精确地融合不同模态传感器的时空信息，以及在复杂驾驶场景下深化冗余验证、目标检测及环境模型构建的研究。随着智能驾驶级别的提升和车载传感器数量的增长，低成本且高性能的传感技术变得至关重要，这要求算法必须具备卓越的环境适应性和理解力。因此，解决多传感器数据融合问题不仅需要攻克技术难题，还需同步优化传感器性能、适应环境变化并改进硬件配置设计，确保系统在任何驾驶情境中均能实现精准感知和高效决策。
4.5 发展建议与未来展望
尽管智能车辆的环境感知传感器技术已取得显著进步，但随着汽车智能化程度不断进化，对其环境感知精确度及实时性需求呈现日益增强的趋势。基于本文对智能驾驶环境感知传感器技术的探究与分析，预测该领域在未来可以围绕以下6大核心方向持续演进与发展：
（1）寻求和研制具有高灵敏度、高稳定性和大动态范围的新型传感材料与器件，以大幅提升车载摄像头、毫米波雷达等传感器的基础性能。
（2）开发智能自适应传感器技术，使其能根据复杂环境自主调整参数，例如自适应光学系统可随光照变化自动优化镜头配置，毫米波雷达采用智能调谐以确保各种工况下的最优性能。
（3）研发节能型硬件结构和精细化能源管理系统，在保证传感器高性能表现的前提下，最大程度减少能耗，延长使用寿命，满足车载传感器持久可靠运行的要求。
（4）加强故障诊断与冗余备份技术的研究，建立全面传感器健康监测与故障识别体系，并设计有效多传感器冗余配置方案，以实现在单个传感器故障时，其余传感器迅速替补，保持系统运行连续稳定。
（5）发展高效精准的实时校准技术和方法，对传感器进行定期或连续校准，这对于提升车载多模态传感器数据一致性并增强整体感知系统的性能至关重要。
（6）聚焦环境影响抑制技术升级，深入研究并有效应对温度、湿度、风速等环境变量对车载传感器性能的干扰，开发耐极端环境的封装技术及高级抗扰信号处理算法，确保传感器在恶劣条件下仍能保持稳定准确的工作状态。
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doi: 10.19822/j.cnki.1671-6329.20240105
  • 首发时间：2025-11-10
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2种不同金属材料的力学参数
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Number of
species		 占总种数比例
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total species (%)		 属
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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