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ADAS自动驾驶产业链报告之(七):L4初创企业篇
字数:0.0万 页数:205 图表数:0
中文电子版:15000元 中文纸版:12000元 中文(电子+纸)版:15500元
英文电子版:3600美元 英文纸版:3800美元 英文(电子+纸)版:3900美元
编号:CYH078 发布日期:2018-08 附件:下载

        佐思产研撰写了《2018年 ADAS与自动驾驶产业链报告》系列,细分为七份报告,合计约1400页。

这七份产业链报告是:

《2018  ADAS与自动驾驶产业链研究——计算平台与系统架构篇》
《2018  ADAS与自动驾驶产业链研究——主机厂与系统集成商篇》
《2018  ADAS与自动驾驶产业链研究——汽车视觉产业篇》
《2018  ADAS与自动驾驶产业链研究——汽车雷达产业篇》
《2018  ADAS与自动驾驶产业链研究——低速自动驾驶产业篇》
《2018  ADAS与自动驾驶产业链研究——商用车自动驾驶产业篇》
《2018  ADAS与自动驾驶产业链研究——L4初创企业篇》

        《2018 ADAS与自动驾驶产业链研究——L4初创企业篇》共约205页,主要研究L4级自动驾驶的初创企业,以及服务于L4级自动驾驶的高精度地图和V2X行业。

        本系列报告的前五篇主要介绍了已经商业化的ADAS、视觉、汽车雷达、计算平台、系统集成领域,和很快就能商业化的低速自动驾驶。而最后两篇研究的是最后商业化的商用车自动驾驶和L4级乘用车自动驾驶市场。

        一直以来,自动驾驶的实现路径上就存在两大阵营:A阵营是以欧洲和亚洲主机厂为主的渐进式路线,从L2、L3循序渐进的演化到L4、L5;B阵营是以谷歌为代表的激进式路线,直奔L4以上。

        2018年,A阵营更加坚定的认为L3绕不过去,L2到L3之间派生出L2.5和L2.75,L3和L4之间派生出L3.5。为了保证人机共驾的可靠性,对驾驶员的监测成为重要课题。

        而B阵营也更加信心十足,WAYMO估值攀升到1750亿美元,启动了数万台无人车的上路测试。

        由于WAYMO无人车的设计运营范围(ODD)目前只有几百平方公里,而A阵营的L2-L3级自动驾驶车可以应用在大多数道路上。短期内两大阵营还会相安无事。

        L4市场的发展,就是ODD区域不断扩大的过程。适合L4无人车运行的ODD区域,需满足多组条件:物理基础设施、运行限制、目标识别、联网条件、环境条件、Zones等。

初创企业篇.png

        一般认为,适合L4无人车运行的ODD区域的安全可靠性最少要达到5个9以上,即99.999%,这就需要厘米级的高精度定位,以及小于5ms的数据传输时延。L4无人车要在开放道路上安全运行,除了自带数十个传感器,还需要高精度地图和5G V2X的支持。

初创企业1.png

        因此在2018年7月,WAYMO CEO John Krafcik坦言:L4自动驾驶车辆普及的时间周期将比想象的要长。

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        而5G V2X的部署,要2020年以后了。

初创企业篇3.png

         L4要从限定场景走向开放道路,还需要跨过至少四个技术门槛。其一,强大计算平台的可量产;其二,传感器感知能力的增强和成本的下降;其三,配套技术标准的完善;其四,配套基础设施的不完善。L4自动驾驶初创企业,在未来两三年仍然需要依靠融资生存。

        计算平台和传感器方面,我们前面几份报告中已经有所讨论。但是L4的发展,会影响到传感器企业的现有格局。

        由于传感器成本太高,于是WAYMO自己开发全部传感器系统,包括激光雷达。通用Crusie 收购激光雷达公司Strobe,福特Argo收购激光雷达公司Princeton Lightwave。WAYMO自研Lidar降低成本90%, Cruise称将整个传感器集中到一个芯片上,Strobe系统可以将其激光雷达成本降低99%。

        不仅是传感器,自动驾驶领先企业连核心计算芯片都自己做,WAYMO、特斯拉、百度都在自己开发核心AI芯片。

        国内新兴造车企业奇点科技表示:传统汽车设计在智能驾驶方面的功能都是分离式设计,数据无法互通,也就无法组合实现多场景化的功能。换句话说,做前视ADAS的是一家公司,有一套传感器;做自主泊车的往往是另一家公司,用另一套传感器。而两家的传感器数据无法打通,造成资源的浪费。 奇点汽车从最初就采用集成式设计,在同一套传感器上自己实现十几种ADAS功能。同时,集成化设计更利于后期的OTA更新。

        在一体化集成化趋势下,传统ADAS公司和传感器企业需重新考虑在L4时代的市场定位了。

        从L2演进到L4,传感器从几个增加到十几个甚至几十个,带来数据流量的飙升。配套设施的完善,主要是指感知系统的完善,包括高精度地图和V2X的引入。高精度地图和V2X同样带来巨大的数据流。各种感知系统的数据汇总起来,使得对于自动驾驶数据流的获取、融合及处理,成为产业竞争合作的焦点。

        传感器(包括高精度地图)数据采集和传输的标准不统一,会制约产业的发展。因此ADASIS、SENORIS、SIP-ADUS、CAICV HD MAP WG、ONEMAP等标准组织相继启动。

初创企业篇4.png

        2018年,是自动驾驶产业链继续完善,各路资本继续涌入的一年。随着L4的市场前景越来越变得可见,为L4配套的高精度地图和V2X,成为企业布局和资本追逐的热点。
 
        佐思产业研究院通过1400页的七份产业链报告,全面梳理了自动驾驶产业数百家企业,透视了产业全貌,也因此发现不少的企业布局存在诸多问题。最典型的问题是:布局太大、定位不清晰、与产业链脱节,缺乏安全策略等。

        如下图所示,自动驾驶产业链如此之复杂,全面掌握行业发展趋势,对于任何一家企业都是一个挑战。

初创企业篇5.png

        L4市场规模将比L2大数十倍,但是L4在中国的成熟将需要五年以上的时间。佐思产研将持续跟踪自动驾驶和ICV产业,每周推出一份周报,每月推出10份研究月报,可帮助企业看清发展方向,了解竞争格局,把握智能网联和自动驾驶市场机会。

初创企业篇6.png


ADAS and Autonomous Driving Industry Chain Report 2018 (VII) - L4 Autonomous Driving Startups at 205 pages in length focuses on researching L4 autonomous driving startups as well as HD map and V2X for L4 autonomous driving.

Of the report series (seven reports), the previous five introduce commercialized ADAS, vision, automotive radar, computing platform, system integration, and low-speed autonomous driving which is to be commercially available soon. The last two reports highlight eventually to-be-commercialized commercial vehicle automated driving and L4 passenger car autonomous driving, respectively.

There have long been two camps in the implementation path of automated driving: Camp A mainly comprised of European and Asian OEMs advocates a progressive path evolving from L2 and L3 to L4 and L5 step by step; Camp B represented by Google stands for a radical path going straight to L4 and above.

In 2018, Camp A believes more firmly that L3 cannot be avoided and L2.5 and L2.75 should be derived from between L2 and L3, and L3.5 from between L3 and L4. To secure the reliability of human and computer driving together, it becomes an important subject to monitor human driver.

Camp B is more confident as well, as WAYMO sees its market capitalization climb to USD175 billion and tests tens of thousands of self-driving cars on roads.

The operational design domain (ODD) of WAYMO self-driving car is confined to just hundreds of square kilometers for the moment; L2-L3 self-driving cars at Camp A can travel on most roads. So the two camps will continue to live in peace with each other in the short run.

In July 2018, John Krafcik, WAYMO’s CEO, admitted that it would take a longer time than expected for the prevalence of autonomous vehicles.

初创1_副本.png

There are at least four technical barriers needing to be surmounted in pushing ahead with L4 from designated scenarios to public roads: first, mass-production of powerful computing platforms; second, stronger sensing capabilities and lower cost of sensors; third, improvement of related technical standards; fourth, inadequate infrastructure. L4 automated driving start-ups will still depend on raised funds to survive in the next two to three years.

We have discussed computing platform and sensor in the previous reports. But L4 development will affect the existing landscape of sensor companies.

Considering too high sensor cost, WAYMO develops by itself all sensor systems it needs, including LiDAR. GM Crusie bought Strobe, a LiDAR company, and Ford Argo acquired Princeton Lightwave, a company engaged in LiDAR. WAYMO can cut 90% cost by developing LiDAR independently; GM Cruise indicates that it can use Strobe’s system to integrate all sensors into one chip, lowering LiDAR cost by 99%.

In addition to sensors, the automated driving leaders also design core computing chips themselves, for example, WAYMO, Tesla and Baidu are all developing their own AI-powered chips.

Singulato, an emerging Chinese automaker indicates that: conventional automotive design is a kind of separate design when it comes to intelligent driving capabilities, that is, separate data cannot be combined for multi-scenario application. In other words, a front ADAS company has a set of sensors of its own and another automated parking company also uses different sensors from others. They cannot share sensor data, which means the waste of resources. Singulato adopts integrated design at the beginning, using same sensors to implement more than a dozen of ADAS functions. And such design also makes subsequent OTA update easier.

Against the backdrop of growing integration, traditional ADAS and sensor companies need to rethink their market orientation in an era of L4.

The number of sensors grows to a dozen and even dozens in the evolution from L2 to L4, generating a data traffic surge. Improvement in supporting facilities, mainly a better perception system, includes introduction of HD map and V2X, which also bring about massive data flow. Data confluence of various perception systems make acquisition, fusion and processing of autonomous driving data flow a focus in industrial competition and cooperation.

Absence of a universally accepted standard for acquisition and transmission of sensor (including HD map) data hinders the development of the industry. Hence, standards organizations like ADASIS, SENORIS, SIP-ADUS, CAICV HD MAP WG and ONEMAP have been initiated.

 初创2_副本.png

The year 2018 sees continued improvement in autonomous driving industry chain and influx of capital. As the market prospects of L4 become more visible, HD map and V2X, the auxiliaries of L4, are chased by enterprises and capital.

ResearchInChina tries to make an overall view of several hundreds of enterprises in autonomous driving industry and present a full picture of the industry via seven industrial-chain reports, 1,400 pages in total, whilst many problems are found, such as irrational layout, unclear orientation, disconnection from industrial chain, and lack of security policy.

As shown in the following diagram, the autonomous driving industry chain is so complicated that it’s a challenge for any enterprise to have a overall grasp of development trends.

 初创3_副本.png

Dozens of times larger than the L2 market, the L4 market will take more than five years to grow mature in China. Tracking autonomous and ICV industry, ResearchInChina will release a weekly report every week and ten monthly reports every month, helping enterprises to see where the industry goes, take in competitive landscape, and seize opportunities in intelligent & connected and autonomous driving markets.

初创4_副本.png

 

第一章 L4自动驾驶综述

第二章 L4自动驾驶初创公司研究
2.1 Waymo
2.1.1 Waymo发展历程
2.1.2 Waymo在自动驾驶领域的投入
2.1.3 Waymo大规模测试验证自动驾驶安全
2.1.4 自动驾驶模拟系统 Carcraft
2.1.5 Waymo自动驾驶系统构成
2.1.6 Waymo的计算平台
2.1.7 Waymo的合作伙伴
2.1.8 Waymo无人出租车运营
2.2  GM Cruise
2.2.1 Cruise自动驾驶系统
2.2.2 Cruise自动驾驶基本模块
2.2.3 Cruise AV主要传感器分布
2.2.4 测试项目和生产基地分布
2.2.5 自动驾驶共享出行布局
2.3 Drive.ai
2.3.1 无人车配置
2.3.2 无人车落地三阶段
2.4 ZMP
2.4.1 RoboCar MiniVan
2.4.2 RoboCar MV 2
2.4.3 2017-2018年营收
2.4.4 进入中国市场
2.5 NuTonomy
2.5.1 测试情况
2.5.2 合作伙伴
2.6 Argo.ai
2.6.1 收购Princeton Lightwave
2.6.2 GeigerCruizer
2.7 Momenta
2.7.1 主要技术和产品
2.7.2 无人车产品战略
2.7.3 高精度地图
2.8 Pony.ai
2.8.1 发展历程
2.8.2 主要技术及测试
2.8.3 研发和运营布局
2.9  景驰科技
2.9.1 核心团队
2.9.2 主要技术
2.9.3 发展历程
2.9.4 未来规划
2.10 Aurora
2.10.1 创始团队
2.10.2 发展动向
2.11 Zoox
2.11.1 主要技术
2.11.2 主要产品
2.11.3 发展战略
2.12 AImotive
2.12.1 全球分布&合作伙伴
2.12.2 融资和发展历程
2.12.3 产品
2.12.4 技术特点
2.12.5 自动驾驶能力发展时间表
2.13 Roadstar.ai
2.13.1 发展历程
2.13.2 系统架构和商业模式
2.14 AKKA
2.15 禾多科技
………………

第三章 高精度地图产业综述
3.1 自动驾驶需要的地图
3.1.1 ADAS MAP
3.1.2 HAD MAP
3.1.3 HD MAP
3.1.4 动态地图
3.1.5 HD MAP形式多样化
3.2 高精度地图的作用
3.2.1 高精度地图用于车辆定位
3.2.2 HD MAP应用于路径规划和感知
3.2.3 动态地图的作用
3.3 高精度地图相关标准
3.3.1 自动驾驶数据链和生态
3.3.2 自动驾驶数据链标准制定情况
3.3.3 NDS
3.3.4 ADASIS
3.3.5 ADASIS V3
3.3.6 SENSORIS
3.3.7 CAICV HD MAP WG
3.4 高精度地图的生产
3.4.1 高精地图生产流程
3.4.2 静态地图数据制作
3.4.3 动态地图数据更新
………………

第四章 高精度地图相关公司研究
4.1 Here
4.1.1 Here发展历程
4.1.2 Here汽车领域布局
4.1.3 Here HD Live Map
4.1.4 Here OTA方案
4.1.5 Self-learning HD map
4.1.6 Here与OneMap
4.1.7 Here高精度地图的拓展
4.2 TomTom
4.2.1 TomTom全球分布
4.2.2 TomTom营业收入
4.2.3 收入结构
4.2.4 汽车类业务
4.2.5 远程信息处理业务
4.2.6 TomTom地图发展情况
4.2.7 TomTom高精度地图
4.2.8 高精度地图的布局和拓展
4.2.9 解决自动驾驶车辆乘客晕车问题
4.3 高德地图
4.3.1 高精地图分级采集系统
4.3.2 高精度地图数据采集车
4.3.3 高精地图技术路线图
4.4 百度地图
4.4.1 高精度地图业务
4.4.2 Apollo高精地图文件结构
4.4.3 Apollo实时相对地图
4.5 四维图新
4.5.1 四维图新发展历程
4.5.2 四维图新全球客户
4.5.3 四维图新车联网业务
4.5.4 高精地图业务的发展路径
4.5.5 高精度地图发展现状
4.5.6 高精地图技术方案
4.5.7 高精地图数据规范
4.6 宽凳科技
4.6.1 宽凳科技技术方案
4.6.2 宽凳高精度地图
4.7 Deep Map
4.7.1 融资及产品
4.7.2 3D高精度地图技术方案
4.8 Civil Maps
4.8.1 3D地图技术方案
4.8.2 与Arm合作自动驾驶导航及定位方案
4.8.3 与Renovo合作
4.9 lvl 5
4.9.1 高精度地图绘制方案
4.9.2 lvl 5高精度地图三个层次
4.10 Carmera
4.10.1 Carmera合作伙伴及合作项目
4.10.2 自动驾驶3D地图解决方案
4.11 Wayz.ai
4.12 Ushr
4.13 DeepMotion
4.14 中海庭
4.15 千寻位置
4.16 Dynamic Map Planning
………………

第五章 V2X产业综述
5.1 V2X基础
5.1.1 为何需要V2X
5.1.2 汽车通信的主要技术
5.1.3 V2X通信体系结构
5.1.4 V2X生态链和标准
5.1.5 各国政府对V2X产业的推动
5.1.6 V2X使用案例
5.1.7 全球C-V2X实验
5.1.8 国内第一个V2X应用层团体标准
5.2 V2X的发展阶段
5.2.1 V2X在自动驾驶中应用的时间线
5.2.2 3GPP V2X 标准进展
5.2.3 3GPP V2X第一阶段
5.2.4 3GPP V2X第二阶段和第三阶段
5.2.5 C-V2X(V2V/V2I)部署时间表
5.3 5GAA
………………

第六章 V2X公司研究
6.1 Sierra Wireless
6.2 Telit
6.3 Cohda Wireless
6.4 Savari
6.5 星云互联
………………

1 Overview of L4 Autonomous Driving

2 L4 Autonomous Driving Startups
2.1 Waymo
2.1.1 Development Course
2.1.2 Investments in Autonomous Driving Field
2.1.3 Large-scale Testing and Verification of Autonomous Driving Safety
2.1.4 Autonomous Driving Simulation System Carcraft
2.1.5 Autonomous Driving System Composition
2.1.6 Computing Platform
2.1.7 Partners
2.1.8 Operation of Driverless Taxicab
2.2 GM Cruise
2.2.1 Cruise Autonomous Driving System
2.2.2 Basic Modules of Cruise AV
2.2.3 Deployment of Major Sensors on Cruise AV
2.2.4 Testing Projects and Production Bases
2.2.5 Layout in Autonomous Ride-sharing Mobility
2.3 Drive.ai
2.3.1 Configurations of Autonomous Vehicle
2.3.2 Three Stages for the Landing of Autonomous Vehicle
2.4 ZMP
2.4.1 RoboCar MiniVan
2.4.2 RoboCar MV 2
2.4.3 Revenue in 2017-2018
2.4.4 Entry to Chinese Market
2.5 NuTonomy
2.5.1 Testing
2.5.2 Partners
2.6 Argo.ai
2.6.1 Acquisition of Princeton Lightwave
2.6.2 GeigerCruizer
2.7 Momenta
2.7.1 Key Technologies and Products
2.7.2 Product Strategy for Autonomous Vehicle
2.7.3 HD Map
2.8 Pony.ai
2.8.1 Development History
2.8.2 Key Technologies and Tests
2.8.3 R&D and Operation Layout
2.9 JingChi
2.9.1 Core Team
2.9.2 Key Technologies
2.9.3 Development Course
2.9.4 Future Planning
2.10 Aurora
2.10.1 Founding Team
2.10.2 Trend of Development
2.11 Zoox
2.11.1 Key Technologies
2.11.2 Major Products
2.11.3 Development Strategy
2.12 AImotive
2.12.1 Global Presence and Partners
2.12.2 Financing and Development History
2.12.3 Products
2.12.4 Technical Features
2.12.5 Timeline for Autonomous Driving Capabilities
2.13 Roadstar.ai
2.13.1 Development Course
2.13.2 System Architecture and Business Model
2.14 AKKA
2.15 HoloMatic

3 HD Map Industry
3.1 Map for Autonomous Driving
3.1.1 ADAS Map
3.1.2 HAD Map
3.1.3 HD Map
3.1.4 Dynamic Map
3.1.5 Diversified Forms of HD Map
3.2 Role of HD Map
3.2.1 HD Map Applied in Vehicle Positioning
3.2.2 HD Map Applied in Path Planning and Perception
3.2.3 Role of Dynamic Map
3.3 Standards about HD Map
3.3.1 Autonomous Driving Data Chain and Ecology
3.3.2 Constitution of Autonomous Driving Data Chain Standards
3.3.3 NDS
3.3.4 ADASIS
3.3.5 ADASIS V3
3.3.6 SENSORIS
3.3.7 CAICV HD MAP WG
3.4 Production of HD Map
3.4.1 Production Flow of HD Map
3.4.2 Data Production of Static Map
3.4.3 Data Updates of Dynamic Map

4 HD Map-related Companies
4.1 Here
4.1.1 Development Course
4.1.2 Layout in Automotive Field
4.1.3 Here HD Live Map
4.1.4 Here OTA Solutions
4.1.5 Self-learning HD map
4.1.6 Here and OneMap
4.1.7 Expansion in HD Map
4.2 TomTom
4.2.1 Global Footprint
4.2.2 Revenue
4.2.3 Revenue Structure
4.2.4 Automotive Business
4.2.5 Telematics Business
4.2.6 Map Development
4.2.7 HD Map
4.2.8 Layout in HD Map and Expansion
4.2.9 To Solve the Problem of Occupant’s Carsickness in Autonomous Vehicle
4.3 AutoNavi (amap.com)
4.3.1 Hierarchical Acquisition System of HD Map
4.3.2 HD Map Data Acquisition Car
4.3.3 HD Map Technology Roadmap
4.4 Baidu Map
4.4.1 HD Map Business
4.4.2 Apollo HD Map File Structure
4.4.3 Apollo Real-time Relative Map
4.5 NavInfo
4.5.1 Development Course
4.5.2 Global Customers
4.5.3 Telematics Business
4.5.4 Development Path of HD Map Business
4.5.5 Status Quo of HD Map
4.5.6 Technical Solutions to HD Map
4.5.7 Data Specifications for HD Map
4.6 KuanDeng Technology
4.6.1 Technical Solutions
4.6.2 HD Map
4.7 Deep Map
4.7.1 Financing and Products
4.7.2 Technical Solutions to 3D Map
4.8 Civil Maps
4.8.1 Technical Solutions to 3D Map
4.8.2 Cooperation with Arm in Autonomous Driving Navigation and Positioning Solutions
4.8.3 Cooperation with Renovo
4.9 lvl 5
4.9.1 HD Map Drawing Scheme
4.9.2 Three Levels of lvl 5 HD Map
4.10 Carmera
4.10.1 Partners and Cooperative Projects
4.10.2 Autonomous Vehicle 3D Map Solutions
4.11 Wayz.ai
4.12 Ushr
4.13 DeepMotion
4.14 Wuhan KOTEI Big Data Corporation
4.15 Qianxun SI
4.16 Dynamic Map Planning

5 V2X Industry
5.1 Fundamentals of V2X
5.1.1 Why to Need V2X
5.1.2 Key Technologies for Vehicle Communications
5.1.3 The Architecture of V2X Communications
5.1.4 V2X Ecosystem and Standards
5.1.5 Countries’ Support for V2X Industry
5.1.6 V2X Use Cases
5.1.7 C-V2X Experiments Worldwide
5.1.8 China’s First V2X Application Layer Group Standard
5.2 Development Stages of V2X
5.2.1 Timeline for V2X Applied in Autonomous Driving
5.2.2 Progress of 3GPP V2X Standards
5.2.3 The First Stage of 3GPP V2X
5.2.4 The Second and Third Stages of 3GPP V2X
5.2.5 Timeline for C-V2X (V2V/V2I) Deployment
5.3 5GAA

6 V2X Companies
6.1 Sierra Wireless
6.2 Telit
6.3 Cohda Wireless
6.4 Savari
6.5 NEBULA Link

 

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