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2018 ADAS和自动驾驶产业链报告(一)计算平台与系统架构
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编号:ZYW237 发布日期:2018-07 附件:下载

《2018 ADAS与自动驾驶产业链研究——计算平台与系统架构篇》共152页,包括七部分内容:

20120114.gifADAS与自动驾驶简介
20120114.gifADAS与自动驾驶市场预测
20120114.gif国内车厂ADAS与自动驾驶策略,包括吉利、通用、上汽、东风、长城、广汽、长安、蔚来、小鹏、拜腾等厂家
20120114.gifADAS与自动驾驶软件架构,包括Autosar经典与自适应、ROS 2.0与QNX
20120114.gifADAS与自动驾驶硬件架构,包括车载以太网、TSN、以太交换与网关、域控制器
20120114.gifADAS和自动驾驶安全认证,包括ISO26262、AEC-Q100
20120114.gif处理器厂家研究,包括NXP、瑞萨、德州仪器、Mobileye、英伟达、安霸、英飞凌和ARM等

        根据佐思产研的研究,2017年中国ADAS与自动驾驶市场规模约为59亿元,到2021年预计能达到426亿元,年均增长率约为67%。

计算平台与系统构架.png

        最先起步的细分市场是汽车视觉、毫米波雷达、ADAS系统,其中毫米波雷达市场的规模和增速均让人惊讶。紧接着是低速自动驾驶,而激光雷达、商用车自动驾驶、乘用车自动驾驶的市场放大将相对滞后。

        汽车进入ADAS与自动驾驶时代后,产品迭代速度急速增加,而汽车市场远不及消费类电子市场广阔,但设计难度、设计与生产成本都高于消费类电子市场,加上产品迭代速度大增,产品生命周期缩短,汽车ADAS与自动驾驶处理器风险大大增加,必须有足够的财力和人力来支持开发汽车ADAS与自动驾驶处理器,全球范围内仅有NXP和瑞萨等极少数几家企业能开发全系列ADAS与自动驾驶处理器。

        安全认证方面,自动驾驶芯片都至少要求ASIL B级水准。目前能达到ASIL B级安全认证的自动驾驶处理器只有瑞萨R-CAR H3等。GPU是通用型设计,而非汽车专用设计,从设计出发点就很难达到ISO26262认证的安全等级。ASIL的认证周期长达2-4年。

        双目的可靠性、准确度、功能性远在单目之上,但是由于双目必须使用FPGA,造价高。成本限制双目只能用在豪华车领域,随着瑞萨和NXP硬核双目处理器的出现,双目将大量出现在ADAS与自动驾驶领域,从豪华车走向中档车。

        随着传输数据的暴增,车载以太网将成为未来汽车的标准配置,而自动驾驶则离不开以太网关或以太交换机。

        Autosar将成为ADAS与自动驾驶领域的标准配置。

计算平台与系统构架1.png

        CNN/DNN图形类机器学习,数据和顺序无关,GPU是最合适的,特别是在性价比方面,英伟达的GPU可用于汽车之外的多个领域,出货量远比汽车专用的ASIC要高,性价比优势十分明显。TPU以降低运算精度来提升速度,并降低功耗,功耗只有GPU的10%。

        RNN/LSTM/强化学习有关顺序类的机器学习,FPGA具备明显优势,尤其是功耗方面,同样性能下FPGA不到GPU的1/5。但FPGA缺乏性价比,高性能的FPGA成本很高。FPGA也可以处理图形类机器学习,可以降低精度来提升性能。

        ASIC性能与功耗比最好,但开发周期长,开发成本最高,灵活性最差,如果出货量低的话(如果采用7纳米工艺,最低也要每年1.2亿的出货量),要么单价很高,要么厂家亏损。大部分深度学习图形类机器学习ASIC都近似TPU。

        车载领域,功耗、性价比都是关键因素,图形类机器学习,GPU是毫无疑问的赢家。但随着算法的不断改进,对运算精度要求越来越低,FPGA的低功耗使得在图形类机器学习领域也有一席之地。顺序类机器学习,FPGA具备压倒性优势。

        自动驾驶可以分为两种类型,一种是Waymo为代表,已经解决了环境感知领域内的大部分问题,精力主要放在行为决策上,计算架构是CPU+FPGA,一般都是英特尔至强12核以上CPU加Altera或Xilinx的FPGA。另一种以Mobileye为代表,还未解决环境感知的全部问题,精力主要在环境感知上,计算架构是CPU+GPU/ASIC。

        展望未来,短期内CPU+GPU会是主流,但长期来看CPU+FPGA/ASIC可能会是主流,主要是算法的改进和传感器特别是激光雷达性能的提升,对图形类运算精度可以持续降低,这对FPGA很有利,而GPU的功耗很难下降。FPGA更容易达到车规级要求。

        芯片代工领域,台积电拿下所有的7纳米订单,包括独家供应苹果的A12,这也是台积电首次超越英特尔成为半导体制造工艺最先进的厂家,像人工智能自动驾驶类强调运算能力的数字类逻辑芯片,先进工艺是必须采用的。


ADAS and Autonomous Driving Industry Chain Report 2018 (I) - Computing Platform and System Architecture underscores the followings:

20120114.gifIntroduction to ADAS and autonomous driving;
20120114.gifADAS and autonomous driving market forecast;
20120114.gifADAS and autonomous driving strategy of carmakers including Geely, GM, SAIC, Dongfeng, Great Wall, GAC, Chang’an, NIO, Xpeng and BYTON;
20120114.gifSoftware architecture of ADAS and autonomous driving, including AUTOSAR Classic and Adaptive, ROS 2.0 and QNX;
20120114.gifHardware architecture of ADAS and autonomous driving, including automotive Ethernet, TSN, Ethernet switch and gateway, and domain controller;
20120114.gifSafety certification of ADAS and autonomous driving, including ISO26262 and AEC-Q100;
20120114.gifStudy into processor firms, including NXP, Renesas, Texas Instruments, Mobileye, Nvidia, Ambarella, Infineon and ARM.

According to Shujubang Consulting, the Chinese ADAS and autonomous driving market was worth about RMB5.9 billion in 2017 and is expected to reach RMB42.6 billion in 2021 at an AAGR of 67% or so.

 平台 1_副本.png

Automotive vision, MMW radar and ADAS are the market segments that develop first with the MMW radar market enjoying an impressive growth rate, closely followed by low-speed autonomous driving. While LiDAR, commercial-vehicle autonomous driving and passenger-car autonomous driving markets lag behind.

As the automobile enters an era of ADAS and autonomous driving, product iteration races up and lifecycle of products is shortened. The automotive market is far smaller than consumer electronics market but sees bigger difficulty in design and higher design and production costs than that in consumer electronics market. Thus automotive ADAS and autonomous driving processor is confronted with higher risks. Hence adequate financial and human resources are required to support the development of automotive ADAS and autonomous driving processors. Globally, only very a few enterprises like NXP and Renesas are capable of developing whole series of ADAS and autonomous driving processors.

With regard to safety certification, autonomous driving chips must attain ASIL B at least, a level only Renesas R-CAR H3 has reached for now. As GPU is a universal design and not car-dedicated design, it is hard to reach the certified safety level of ISO26262 from the point of design. The certification cycle of ASIL is up to two to four years.

Reliability, precision and functionality of stereo camera are well above that of mono camera, but as the stereo camera must use FPGA, it costs much. High costs restraint the application of the stereo camera only on luxury cars. However, with emergence of Renesas and NXP hardcore stereo processors, the stereo camera will be vastly used in ADAS and autonomous driving field, expanding from luxury models to mid-range ones.

With an explosive growth in data transmission, automotive Ethernet will become a standard configuration of the automobile, and Ethernet gateway or Ethernet switch is indispensable to autonomous driving.

Autosar will act as a standard configuration in ADAS and autonomous driving field.

平台2_副本.png

CNN/DNN graphics machine leaning: GPU is most suitable when data is irrelevant to sequence. Nvidia GPU can be used in multiple fields except for automobile and finds shipments far higher than that of automotive ASIC, enjoying superiority in cost performance. TPU lifts speed and reduces power consumption (only 10% of that of GPU) at the expense of the precision of computation.

RNN/LSTM/reinforcement learning sequence-related machine learning: FPGA has distinct advantages, particularly in power consumption, consuming less than one-fifth of GPU under same performance. However, high-performance FPGA is incredibly costly. FPGA can also process graphics machine leaning and improve performance by reducing precision.

ASIC stands out by performance-to-power consumption ratio but has shortcomings of long development cycle, the highest development cost and the poorest flexibility. The unit price will be very high or firms will make losses if the shipments are small (at least annual shipments of 120 million units if 7-nanometer process is employed). Most ASICs for deep-learning graphics machine learning are similar to TPU.

Power consumption and cost performance are crucial in in-vehicle field. GPU is no doubt a winner in graphic machine learning. However, as algorithms are constantly improved, the ever low requirements on the precision of computation, and low power consumption will ensure a place of FPGA in graphics machine learning. FPGA has overwhelming advantages in sequence machine learning.

Autonomous driving can be divided into two types, one represented by Waymo, which has solved most of the problems concerning environmental perception and concentrates on behavior decision-making with computing architecture of CPU+FPGA (usually Intel Xeon 12-core and above CPU plus Altera or Xilinx’s FPGA; the other represented by Mobileye which has not solved all problems involving environmental perception and concentrates on it with computing architecture of CPU+GPU/ASIC.

CPU+GPU will be the mainstream in the short run, but CPU+FPGA/ASIC may dominate in the long term, largely due to continuous decline in the precision of computation of graphics because of improvement in algorithms and performance of sensors (LiDAR in particular), which is conducive to FPGA, while it is hardly for the power consumption of GPU to fall. It is easier for FPGA to meet car-grade requirements.

In chip contract manufacturing field, TSMC has won all 7-nanometer chip orders, including A12 exclusively provided for Apple, marking for the first time TSMC overtook Intel to become the vendor with the most advanced semiconductor manufacturing process, a must in the production of digital logic chip whose computing capability is underlined in AI autonomous driving.

一、ADAS和自动驾驶简介
1.1 ADAS定义和分类
    ADAS主要功能
1.2 自动驾驶汽车定义和关键技术
1.2.1 环境感知技术:从传感器感知到数据融合
      环境感知技术:不同传感器各有所长
1.2.2 定位技术
1.2.3 路径规划技术
1.2.4 自动泊车技术
1.3 自动驾驶的分级(SAE)
1.4 自动驾驶的分级(中国)
1.5 ADAS和自动驾驶的相关法规和标准
1.5.1《国际道路交通公约》修正案通过,自动驾驶获许可
1.5.2 关于自动驾驶测试法规
1.5.3 欧盟2021年变更新车强制配装功能
1.6 典型无人驾驶框架
1.6.1 第一步,定位
      高精度地图与V2X
1.6.2 第二步,感知
      使用融合路线获得3D Bounding
1.6.3 第三步:预测交通场景
      预测还包含了场景理解
1.6.4 第四步:决策
      车道级全局规划
      并非是路线越短越好
      行为规划behavior planning是最难的
      行为规划算法很多,很多都不成熟
1.6.5 第五步:动作规划
1.6.6 第六步:执行

二、市场规模和预测
2.1 2015-2050年全球自动驾驶汽车销量及预测
2.2 2017-2025年全球ADAS市场年均增长率
2.3 Veoneer:主动安全市场2025年预计达到300亿美元
2.4 2016-2021年中国ADAS与自动驾驶系统市场规模预测
2.5 2017年国内乘用车ADAS累计装配量同期对比:ACC、FCW、LKS增长最快

三、车厂ADAS与自动驾驶策略
3.1 吉利汽车
3.2 通用汽车智能驾驶
3.3 日产、宝马、小鹏的Mobileye路线
3.4 宝马计划在2021年量产的CO-PILOT还是L3
    英特尔无人车使用了32线激光雷达
3.5 长安、一汽、蔚来、上汽的博世路线
3.6 博世自动驾驶解决方案
3.6.1 博世域控制器
      域控制器对比
3.6.2 TJP解决方案
3.6.3 传感器解决方案
3.6.4 高精地图解决方案
3.6.5 商用车自动驾驶规划
3.7 长城的安波福路线
    安波福的道路模型依赖激光雷达
3.8 广汽的电装路线
3.9 现代L4无人车传感器布局
3.10 福特以高线激光雷达为核心传感器
3.11 拜腾与Aurora合作
4 ADAS和自动驾驶软件架构
4.1 ADAS与自动驾驶系统核心元素
4.2 Autosar简介
4.2.1 Autosar路线图
4.2.2 Autosar主要成员
4.2.3 Autosar分经典版和自适应版
4.2.4 经典版Autosar架构
4.2.5 自适应Autosar软件分层及与经典版Autosar对比
4.2.6 自适应Autosar路线图
4.3 ROS是自动驾驶操作系统
4.3.1 ROS得到部分车厂的认同
4.3.2 ROS简介
4.3.3 ROS2.0接近实时
4.3.3 ROS改造
4.3.4 ROS安全
4.4 QNX ADAS 2.0 达到了最高的ASIL D级
4.4.1 QNX ADAS 2.0支持范围
5 ADAS和自动驾驶硬件架构
5.1 典型汽车网络架构
5.2 从中央网关到域控制器结构(NXP)
5.3 未来汽车电子电气架构(博世)
5.4 为什么要用域控制器
5.4.1 当前和未来的汽车电子架构
5.4.2 域控制器共享硬件资源,实现操作系统和基础软件共享
5.4.3 I/O 架构和域控制器(Domain Controller)
5.4.4 域控制器的基础:车载以太网,汽车总线对比
      汽车总线对比
5.5 车载以太网
5.5.1 车载以太网雏形:EAVB
5.5.2 EAVB的下一步:TSN
5.5.3 TSN网络
5.5.4 TSN以太交换机是未来自动驾驶计算系统核心
5.6 Waymo所用的计算系统架构
5.7 英伟达PX2架构
5.8 NXP S32G网关
5.8.1 NXP自动驾驶蓝盒子架构
5.8.2 网关与以太交换
5.9 瑞萨L4运算平台架构
5.9.1 瑞萨对未来汽车电子架构的设想
6 ADAS和自动驾驶安全认证
6.1 车规级芯片认证
6.2 AEC认证
6.3 ISO26262、功能安全与ASIL
6.4 ISO26262流程
6.5 不同的安全等级要求不同的评判独立性
6.6 自动驾驶ECU典型结构,模型部分达B级,规划部分达D级
7 ADAS处理器厂家研究
7.1 ADAS与自动驾驶处理器产业
7.1.1 FPGA/GPU/ASIC/CPU/TPU与机器学习
7.1.2 软核/固核/硬核
7.1.3 固核是主流方向
7.1.4 典型L4运算系统架构
7.2 ARM
7.2.1 ARM自动驾驶汽车应用分布
7.2.2 ARM推荐的自动驾驶SoC设计
7.2.3 ARM A系列
7.2.4 ARM的R系列与M系列
7.3 NXP
7.3.1 NXP 自动驾驶CPU路线图
7.3.2 NXP的ADAS与自动驾驶视觉处理芯片路线图
7.3.3 NXP S32V3简介
7.3.4 NXP S32V3视觉处理系统
7.3.5 NXPADAS底盘控制MCU MPC5746R框架图
7.3.6 NXP 自动驾驶底盘控制MCU为S32D/S系列
7.4 瑞萨
7.4.1 瑞萨R-CAR H3
7.4.2 瑞萨R-CAR V3H
7.4.3 瑞萨RH850/P1H-C
      为底盘控制设计的最高安全级别的MCU
7.4.4 瑞萨与Dibotics合作开发激光雷达应用
7.4.5 瑞萨高精度地图合作伙伴USHR
7.4.6 瑞萨操作系统合作伙伴QNX和滑铁卢大学
7.4.7 瑞萨与Leddartech合作激光雷达
7.4.8 Renesas自动驾驶合作动向
7.5 Nvidia
7.5.1 Nvidia DRIVE系列产品参数对比
7.5.2 PX2电路原理图
7.5.3 Nvidia DRIVE Xavier
7.5.4 Nvidia DRIVE Pegasus
7.6 安霸
7.6.1 安霸技术分布与路线图
7.6.2 安霸核心技术CVflov和双目数据处理硬核
7.6.3 安霸CV2AQ
7.6.4 安霸CV2AQ
7.7 Mobileye
7.7.1 Mobileye Eyeq4/5内部框架图
7.7.2 双EYEQ4的L3方案(经纬恒润)
7.8 德州仪器TDA系列
7.8.1 TDA2系列简介
7.8.2 TDA4与TIDL
7.8.3 德州仪器单芯片毫米波雷达方案
7.8.3 德州仪器单芯片毫米波雷达方案
7.9 英飞凌
7.9.1 英飞凌MEMS激光雷达解决方案
7.9.2 英飞凌毫米波雷达收发器

1 Introduction to ADAS and Autonomous Driving

1.1 Definition and Classification of ADAS
Main Functions of ADAS
1.2 Definition and Key Technologies of Autonomous Vehicle 
1.2.1 Environmental Perception Technology: from Sensor Perception to Data Fusion
Environmental Perception Technology: Different Sensors Have Different Advantages
1.2.2 Positioning Technology
1.2.3 Path Planning Technology
1.2.4 Automatic Parking Technology
1.3 Grading of Autonomous Driving (SAE)
1.4 Grading of Autonomous Driving (China)
1.5 Regulations on and Standards for ADAS and Autonomous Driving
1.5.1 Amendment to the 1968 Vienna Convention on Road Traffic Allows Autonomous Driving
1.5.2 Regulations on Autonomous Driving Tests
1.5.3 EU Lists 11 Automotive Safety Systems to Become Mandatory from 2021
1.6 Typical Framework of Autonomous Driving
1.6.1 First Step, Positioning
HD Map and V2X
1.6.2 Step 2, Perception
3D Bounding with Route Fusion
1.6.3 Step 3: Traffic Scenario Forecast
Forecast Includes Scenario Understanding
1.6.4 Step 4: Decision-making
Lane Overall Planning
Shorter Routes May Be Not Better.
Behavior Planning Is the Most Difficult
There Are Many Behavior Planning Algorithms, Mostly Immature
1.6.5 Step 5: Action Planning
1.6.6 Step 6: Execution

2 Market Size and Forecast
2.1 Global Sales Volume of Autonomous Vehicles, 2015-2050E
2.2 AAGR of Global ADAS Market, 2017-2025E
2.3 Veoneer: Active Safety Market Is Expected to Reach USD30 Billion by 2025
2.4 Chinese ADAS and Autonomous Driving System Market Size, 2016-2021E
2.5 Concurrent Comparison of Domestic Passenger Car ADAS Cumulative Installations in 2017: ACC, FCW and LKS Saw the Fastest Growth Rate

3 Carmakers’ ADAS and Autonomous Driving Strategies
3.1 Geely
3.2 GM Intelligent Driving
3.3 Mobileye Route of Nissan, BMW and Xpeng
3.4 BMW Plans to Mass-produce L3 CO-PILOT in 2021.
Intel’s Driverless Cars Use 32-beam LiDAR
3.5 Bosch Route of Chang’an, FAW, NIO and SAIC
3.6 Bosch’s Autonomous Driving Solutions
3.6.1 Bosch’s Domain Controllers
Comparison between Various Domain Controllers
3.6.2 TJP Solutions 
3.6.3 Sensor Solutions 
3.6.4 HD Map Solutions 
3.6.5 Planning for Commercial Vehicle Autonomous Driving
3.7 Aptiv Route of Great Wall
Aptiv’s Road Model Relies on LiDAR
3.8 Denso Route of GAC
3.9 Layout of Hyundai L4 Driverless Car Sensors
3.10 Ford Uses High-beam LiDAR as the Core Sensor
3.11 BYTON Collaborates with Aurora

4 Software Architecture of ADAS and Autonomous Driving 
4.1 Core Elements of ADAS and Autonomous Driving System
4.2 Introduction to Autosar
4.2.1 Roadmap
4.2.2 Main Members
4.2.3 Classic Version and Adaptive Version
4.2.4 Architecture of Classic Version
4.2.5 Software Stratification of Adaptive Version; Comparison between Classic Version and Adaptive Version
4.2.6 Roadmap of Adaptive Version
4.3 ROS: an Autonomous Driving Operating System
4.3.1 ROS Recognized by Some Carmakers
4.3.2 Introduction to ROS
4.3.3 ROS2.0 Is Close to Real Time
4.3.3 Transformation of ROS
4.3.4 Security of ROS
4.4 QNX ADAS 2.0 Achieves the Highest ASIL D Level
4.4.1 Scope Supported by QNX ADAS 2.0

5 Hardware Architecture of ADAS and Autonomous Driving
5.1 Typical Automotive Network Architecture
5.2 From the Central Gateway to the Domain Controller Structure (NXP)
5.3 Future Automotive Electronic and Electrical Architecture (Bosch)
5.4 Why Use A Domain Controller
5.4.1 Current and Future Automotive Electronic Architecture
5.4.2 Domain Controllers Share Hardware Resources, so that Operating System and Basic Software Realize Sharing
5.4.3 I/O Architecture and Domain Controller
5.4.4 Basis of Domain Controller: Automotive Ethernet, Automotive Bus Comparison
Automotive Bus Comparison
5.5 Automotive Ethernet
5.5.1 Prototype of Automotive Ethernet: EAVB
5.5.2 The Next Step of EAVB: TSN
5.5.3 TSN Network
5.5.4 TSN Ethernet Switch Is the Core of the Future Autonomous Driving Computing System
5.6 The Computing System Architecture Used by Waymo
5.7 NVIDIA PX2: Architecture
5.8 NXP S32G: Gateway
5.8.1 Architecture of NXP Autonomous Driving Blue Box
5.8.2 Gateway and Ethernet Switch
5.9 Architecture of Renesas L4 Computing Platform 
5.9.1 Renesas’ Vision of the Future Automotive Electronic Architecture

6 Safety Certification of ADAS and Autonomous Driving
6.1 Chip Certification in Line with National Automotive Standards
6.2 AEC Certification
6.3 ISO26262, Functional Safety and ASIL
6.4 ISO26262 Process
6.5 Different Safety Levels Require Different Judgmental Independence
6.6 Typical Structure of Autonomous Driving ECU; the Model Part Reaches the B Level; the Planning Part Reaches the D Level

7 ADAS Processor Vendors
7.1 ADAS and Autonomous Driving Processor Industry
7.1.1 FPGA/GPU/ASIC/CPU/TPU and Machine Learning
7.1.2 Soft/Solid/Hard Core
7.1.3 Solid Core Is the Mainstream
7.1.4 Architecture of Typical L4 Computing System
7.2 ARM
7.2.1 Application Structure of ARM Autonomous Vehicles
7.2.2 Autonomous Driving SoC Design Recommended by ARM
7.2.3 ARM A Series
7.2.4 ARM R Series and M Series
7.3 NXP
7.3.1 NXP Autonomous Driving CPU Roadmap
7.3.2 Roadmap of NXP’s ADAS and Autonomous Driving Vision Processing Chip
7.3.3 Introduction to NXP S32V3
7.3.4 NXP S32V3 Vision Processing System
7.3.5 Framework Diagram of NXP ADAS Chassis Control MCU MPC5746R
7.3.6 NXP Autonomous Driving Chassis Control MCU: S32D/S Series
7.4 Renesas
7.4.1 Renesas R-CAR H3
7.4.2 Renesas R-CAR V3H
7.4.3 Renesas RH850/P1H-C
MCU with the Highest Safety Level Designed for Chassis Control
7.4.4 Renesas Cooperates with Dibotics to Develop LiDAR Applications
7.4.5 Renesas Partners with USHR in HD Map
7.4.6 Renesas Teams up with QNX and University of Waterloo in Operating System
7.4.7 Renesas Collaborates with Leddartech on LiDAR
7.4.8 Renesas’ Cooperation in Autonomous Driving
7.5 Nvidia
7.5.1 Parameters of Nvidia DRIVE Series Products
7.5.2 Circuit Schematic Diagram of PX2
7.5.3 Nvidia DRIVE Xavier
7.5.4 Nvidia DRIVE Pegasus
7.6 Ambarella
7.6.1 Technology Distribution and Roadmap
7.6.2 Core Technology CVflov and Stereo-camera Data Processing Hard Core
7.6.3 Ambarella CV2AQ
7.6.4 Ambarella CV2AQ
7.7 Mobileye
7.7.1 Internal Framework Diagram of Mobileye Eyeq4/5
7.7.2 Dual-EYEQ4 L3 Solutions (HiRain Technologies)
7.8 TDA Series of Texas Instruments
7.8.1 Introduction to TDA2 Series
7.8.2 TDA4 and TIDL
7.8.3 Single-chip MMW Radar Solutions
7.9 Infineon
7.9.1 MEMS LiDAR Solutions
7.9.2 MMW Radar Transceivers

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