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全球及中国高精地图行业分析（2022）
市场调查报告书
商品编码
1148286

全球及中国高精地图行业分析（2022）

Global and China HD Map Industry Report, 2022
出版日期: | 出版商: ResearchInChina | 英文 330 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

2022年上半年，中国将有超过10万辆乘用车配备高精地图。高精地图一直以来大多作为选装件，现在逐渐被纳入汽车标配，如立L9、蔚来ET7、高合HiPhi等。

随着主机厂对高精地图安装率的提升，城市地区的应用成为新的领域。地图公司通过加快数据收集和与SD地图技术的结合，积极开发城市场景。

在这份报告中，我们分析了世界和中国的高精地图行业，并提供了高精地图的概述、相关政策法规、高精地图市场规模、结构、商业模式、高精地图相关技术（生产、更新） , 流通等).□集成等), 主要用例, 国内外企业概况, 主要技术和产品。

内容

第 1 章高精度地图政策、标准和法规

  • 与高清地图相关的政策
    • 2022年最新政策：开展智能网联汽车高精地图试点应用
    • 2022 年政策更新：交通运输部批准实施百度高精地图试点
    • 2022年最新政策：上海加快智能网联汽车创新发展
  • 高清地图的规定
    • 关于高精地图的外国法规
    • 高清地图的国内法规
    • 2022年最新规定：逐步加强高精地图製图资质审核（一）
    • 2022年最新规定：逐步加强高精地图製图资质审查（2）
    • 2022 年法规更新：自然资源部澄清製造汽车传感器和智能网联汽车属于非法测绘活动 (3)
    • 2022最新规定：重庆市智能网联汽车高精地图管理办法（4）
  • 与高精地图相关的标准
    • 海外地图标准生态：欧洲
    • 海外地图标准生态：日本
    • 国外高精地图标准制定：现状
    • 中国高精地图标准定制现状（已发表）
    • 中国高精地图标准定制现状（初步调查）
    • 推进中国高精地图标准：逐步完善众包更新标准
    • 2022最新标准《道路交叉口交通信息全息采集系统通用规范》团体标准正式发布
    • 2022年最新标准：国内首张高清电子导航地图国内道路质量规范和行业标准认证（二）
    • 2022 年最新标准：即将推出国内市场自动停车地图标准 (3)
    • 2022 年最新标准：首部智能网联汽车地图省级地方标准发布 (4)
  • 高精地图合规性
    • 该部加强了对车辆数据的安全控制
    • 高精地图数据合规性和生成流程
    • 高精地图合规数据服务
    • 高精地图合规发展之路：打造高精动态地图基础平台

第2章高精地图市场规模与竞争格局

  • 高精地图市场规模
    • 2022 年，中国的自动驾驶汽车将安装高精地图
    • 中国乘用车OEM高精地图市场规模(1)
    • 中国乘用车OEM高精地图市场规模(2)
  • 高精度地图市场的竞争格局
    • 高清地图的市场模式
    • 高精地图企业（1）：国内传统地图供应商前10名（1）
    • 高精地图企业（1）：国内传统地图供应商前10名（2）
    • 高精地图公司（一）：各大地图公司产品对比
    • 高精地图公司（一）：三大地图厂商比较
    • 高精地图公司 (2)：OEM 高精地图布局
    • 高精地图公司 (2)：OEM 倾向于在内部开发高精地图，而不是外包
    • 高精地图公司（2）：OEM 开发自己的高精地图所面临的困难
    • 高精地图公司（二）：OEM高精地图布局案例（一）
    • 高精地图公司（2）：OEM高精地图布局案例（2）
    • 高精地图公司（3家）：国外地图公司
  • 高精地图商业模式
    • 高精地图商业化场景
    • 高精地图商业模式（一）：自动驾驶
    • 高精地图商业模式（二）：停车场
    • 高精地图收入模式分类
    • 高精地图商业模式概览：国内地图公司（一）
    • 高精地图商业模式概述：国内地图公司（二）
    • 高精地图商业模式概述：国外地图公司（一）
    • 高精地图商业模式概述：国外地图公司（二）
    • 高精地图收入模式示例：云 SaaS 服务模式

第3章高精地图量产关键技术

  • 高精地图製作
    • 创建高清地图的流程
    • 高精地图製作流程（一）：城市道路高精地图采集难点
    • 高精地图製作流程（二）：生成点云高精地图
    • 高精地图製作流程（二）：视觉融合点云製图
    • 高清地图创建流程（二）：使用 LiDAR 进行点云映射
    • 高精地图製作流程（三）：特征提取
    • 高精地图製作工具：开源架构Lanelet2
    • 高精地图製作工具：开源架构 OpenDRIVE
    • 高精地图製作实例（一）：百度
    • 高精地图製作实例（二）：四维图新自动化地图生产线
    • 高精地图製作实例（二）：四维图新自动化地图製作新技术
    • 薄弱的高清地图製作流程
    • 弱高精地图製作流程（一）：不同传感器空间的融合
    • 弱高精地图製作流程（二）：环境建模
    • 弱高清地图製作流程（三）：在线高清矢量图重建的实现
    • 高精地图製作技术趋势：标清地图一体化製作
  • 高精地图更新
    • 高精地图更新趋势：从专业采集到众包更新
    • 如何众包更新高清地图
    • 众包高精地图更新的挑战（一）
    • 众包更新高精地图的挑战（二）
    • 高精地图众包更新方案（一）：低成本采集+SLAM算法
    • 高精地图众包更新方案（二）：纯视觉+深度学习+SLAM算法（一）
    • 高精地图众包更新方案（二）：纯视觉+深度学习+SLAM算法（二）
    • 高精地图众包更新方案（二）：纯视觉+深度学习+SLAM算法（三）
    • 高精地图众包更新方案（三）：云+端形态更新闭环
    • 高精地图更新目标（一）：OEM众包更新方案（一）
    • 高精地图更新目标（一）：OEM众包更新方案（二）
    • 高精地图更新挑战（二）：技术提供商众包更新解决方案
    • 高精地图众包更新案例（一）：四维图新地图学习平台（一）
    • 高精地图众包更新案例（一）：四维图新地图学习平台（二）
    • 高精地图众包更新案例（一）：四维图新地图学习平台（三）
    • 高精地图众包更新案例（一）：四维图新独有的数据处理平台
    • 高精地图众包更新案例（二）：TomTom在云端完成众包数据融合
  • 高精地图数据的分布与融合
    • 高精地图数据分布与融合过程
    • 流程 1：高精地图数据传输引擎架构
    • 流程 1：用于高精地图数据传输引擎的道路网络模型
    • 流程 1：高精地图数据传输引擎集成表单
    • 流程 1：高精地图数据传输引擎集成形式的高精地图框
    • 流程 1：领先的高清数据传输引擎提供商
    • 过程 2：高精地图数据格式转换 (1)
    • 流程 2：高精地图数据格式转换 (2)
    • 流程三：高精地图数据分发与接收端交互
    • 过程 4：融合高精地图数据
    • 流程 4：高精地图数据传输和 ADAS 集成
    • 流程 4：高精地图数据传输与 ADAS 应用之间的关係
    • 高精地图数据分布与融合案例（一）：四维图新互相关层
    • 高精地图数据分布与融合案例（二）：高德地图数据融合与应用方法
    • 高精地图数据的分布和融合趋势：在中央域控制器中集中处理
  • 高精地图与V2X技术的融合应用
    • 高精地图在 V2X 中的作用（一）：基础设施
    • 高精地图在 V2X 中的作用（二）：数据支持
    • 高精地图在V2X中的作用（三）：助力实现高精度定位
    • V2X 在高精地图中的作用（一）：数据存储
    • V2X 在高精地图中的作用（二）：数据传输（一）
    • V2X 在高精地图中的作用（二）：数据分发（二）
    • V2X 在高精地图中的作用（三）：更新地图数据
    • 利用高精地图和 V2X 融合：红绿灯提示
  • 应用于车道级定位的高精地图
    • 基于高精地图的车道级定位解决方案的结构
    • 车道级定位方案中的地图匹配技术（一）：基于点云的地图匹配
    • 车道级定位方案中的地图匹配技术（一）：基于点云的地图匹配算法（一）
    • 车道级定位解决方案中的地图匹配技术（一）：基于点云的地图匹配算法（二）
    • 车道级定位解决方案的地图匹配技术（二）：基于深度学习的地图匹配（一）
    • 车道级定位解决方案的地图匹配技术（二）：基于深度学习的地图匹配（二）
    • 提供基于高精地图的车道级定位解决方案
    • 应用实例（一）：Mxnavi——基于高精地图的车道级定位解决方案
    • 用例（二）：Voyah——基于弱高精地图的城市道路高清定位解决方案

第4章高精地图量产利用场景

  • 各种自动驾驶级别对高清地图的需求
    • 各级别自动驾驶对高精地图道路要素的需求
    • 高精地图元素的自动驾驶要求：L2 NOA
    • 自动驾驶对高精地图要素的要求：L2免提
    • 高精地图元素的自动驾驶要求：L3
    • 自动驾驶高精地图要素要求：L4（1）
    • 自动驾驶高精地图要素要求：L4（2）
    • 自动驾驶发展阶段：人车协同驾驶
    • 人车协同驾驶阶段的高精地图框架
    • 人车协同驾驶阶段对高精地图行业的挑战
  • 高精地图使用场景 1：乘用车在高速公路上的自动驾驶
    • 自主品牌量产乘用车加装高精地图（一）
    • 自主品牌量产乘用车加装高精地图（2）
    • 自主品牌量产乘用车加装高精地图（3）
    • 自主品牌量产乘用车加装高精地图（4）
    • 自主品牌量产乘用车加装高精地图（5）
    • 在合资品牌的量产乘用车上安装高清地图
    • 量产实例（一）：广汽永恆之塔高精地图安装需求
    • 量产实例（一）：广汽Aion高精地图解决方案
    • 量产范例（一）：广汽Aion EHP
    • 製作实例（一）：广汽永恆之塔高清地图的曲率和倾斜度
    • 製作实例（二）：小鹏高精地图安装方案
    • 製作实例（二）：小鹏P7高精地图功能
    • 量产实例（二）：利用小鹏实现基于高精地图的城市辅助驾驶
    • 量产范例（三）：长城WEY——用高精地图实现P2P自动驾驶
    • 量产实例（四）：GM-高精地图安装方案
    • 製作实例（5）：理想汽车-高精地图安装
    • 量产实例（六）：NIO——2020年引入高精地图
    • 生产实例（六）：NIO NOP集成高精地图
    • 量产实例（七）：上汽IM自动驾驶硬件方案
    • 量产范例（8）：上汽大通MIFA 9
    • 量产范例（9）：吉利-ZEEKR 001
    • 量产示例（十）：AVATR 11及高速公路+城市场景支持驾驶
    • 製作方案（一）：BMW高精地图安装要求及解决方案（一）
    • 量产计划（一）：BMW高精地图安装需求及解决方案（二）
    • 量产方案（一）：BMW高精地图量产功能
    • 乘用车自动驾驶新战场：城市场景
    • 城市场景自动驾驶高精地图解决方案：SD Pro Map
    • 城市自动驾驶乘用车高精地图引入方案（一）：多传感器融合+高精地图
    • 城市自动驾驶乘用车高精地图引入解决方案（二）：重视地图的感知和减重
    • 感知/地图轻量级应用案例（一）：IDRIVERPLUS
    • 面向感知的地图亮化应用示例（二）：好模AI
  • 高精地图使用场景（二）：小客车低速自动泊车
    • AVP 地图类型 (1)：高清地图
    • AVP地图的种类（一）：SLAM实时地图
    • 前 5 名停车场高清地图供应商
    • 用例 (1)：Roadgrids - 停车场高清地图系统
    • 用例（2）：纵目科技——基于高精地图的停车产品
    • 自动泊车地图发展趋势：车、场、云、APP一图
  • 高精地图应用场景（三）：货运自动驾驶
    • 高精地图在低速自动驾驶中的重要性
    • 低速自动驾驶高精地图开发方法：SLAM
    • 货运自动驾驶高清地图：供应商模式 (1)
    • 货运自动驾驶高清地图：供应商模式 (2)
    • 自动驾驶在货运中的应用（一）：美团无人配送车
    • 自动驾驶在货运中的应用（一）：京东自动驾驶配送车
  • 高精地图使用场景4：自动驾驶载人
    • 高清地图对于先进的自动驾驶至关重要
    • 自动驾驶载人用例（一）：自动驾驶robotaxi高精地图应用
    • 自动驾驶载人交通用例（一）：机器人公交车高精地图应用
    • 用于载人的自动驾驶汽车用例：驱动自动驾驶小巴的 PIX

第5章中外高精地图提供商

  • Baidu Map
  • NavInfo
  • eMapgo
  • Amap
  • Tencent
  • ECARX
  • BrightMap
  • Mxnavi
  • Huawei
  • Heading Data Intelligence
  • JD.com
  • SFMAP Technology
  • Leador
  • Momenta
  • HERE
  • 4 HERE HD Live Map
  • TomTom

第6章 高精地图相关技术公司

  • Mobileye
  • Nvidia
  • Bosch
  • DMP
  • Carmera
  • Kuandeng Technology
  • DeepMotion
  • Dilu Technology
  • 其他
简介目录
Product Code: ZHP124

HD maps have been applied on a large scale, spreading from freeways to cities

According to ResearchInChina, more than 100,000 Chinese passenger cars were equipped with HD maps by OEMs in the first half of 2022. OEMs will constantly speed up the installation of HD maps. HD maps were mainly regarded as an option in the past, but now they have been gradually included in the standard configuration of vehicles, such as Li L9, NIO ET7, HiPhi, etc.

From the perspective of the layout of OEMs, advanced driver assistance in urban scenarios has become a new hot spot in intelligent field. At present, there are three technical routes for advanced driver assistance in cities:

  • (1) Pure vision: Companies represented by Tesla mainly rely on cameras, super powerful algorithms, etc. to realize assisted driving in cities. It is reported that Tesla may introduce FSD Beta to Chinese market.
  • (2) Perception + map: The solution does not depend heavily on pre-made HD maps. It builds real-time HD maps through vision systems in places where there are no HD maps. For example, the LiDAR version of WEY Mocha released by Great Wall in August 2022 adopts Haomo.AI's urban NOH technology with a weak HD map, which require fewer lane-level attributes than regular HD maps.
  • (3) Multi-sensor fusion + HD map: Companies represented by NIO, Li Auto and Xpeng enhance the intelligent driving experience by making use of HD maps and LiDAR to make up the insufficient computing power.

Xpeng expects to gradually introduce urban NGP functions to users in Guangzhou, Shenzhen, Beijing, Shanghai, Hangzhou and other cities since 2022.

After the launch of urban NGP by Xpeng, the point-to-point autonomous driving has been realized to some extent (except that drivers cannot take their hands off the steering wheel), covering more than 90% of daily driving scenarios including parking lots, cities and freeways.

NIO plans to make urban assisted driving possible on models such as ET7 and ET5 equipped with NAD system in 2022. When the driver sets a destination on the navigator, the IVI map shows the start and end sections of NOP. When the vehicle enters the sections, the driver can turn on or off the NOP function through the "Pilot Assist" in the lower left corner of the navigator.

Li L9 equipped with intelligent driving system "Li AD Max" can see navigation and assisted driving in all scenarios.

With the computing power as high as 400TOPS, Avatr 11 equipped with Huawei ADS can secure high-level intelligent driving functions at freeways, urban areas and parking.

BAIC ARCFOX αS HI Advance equipped with Huawei ADS can accomplish autonomous driving on freeways, high-level autonomous driving in urban areas, AVP and other functions.

In terms of mainstream solutions, OEMs except Tesla basically adopt the sensor + map solution, but they have different requirements for map accuracy. As per the development progress of HD maps, urban HD maps face long mileage, surveying and mapping restrictions and update challenges. Therefore, some OEMs consider using SD pro maps for urban assisted driving to avoid HD map elements as much as possible.

Map providers step up the layout of HD maps in urban scenarios

With a higher OEM installation rate of HD maps, the application in urban scenarios has become a new arena. Map players are aggressively deploying urban scenarios, mainly in the following ways:

Faster collection of urban HD map data

The mileage of China's freeways is about 300,000 kilometers, and the mileage of urban roads is close to 10 million kilometers. Mainstream map players have basically completed the collection of HD maps for freeways and urban expressways. Therefore, the production passenger cars equipped with HD maps can realize advanced driver assistance in high-speed scenarios. In the future, map players will make breakthroughs in HD maps for cities to meet the demand of OEMs.

Integration of SD maps and HD maps

In high-speed scenarios, map companies can post-match SD maps with HD maps, with a high accuracy rate. However, in urban scenarios, SD maps and HD maps can't be associated in the later stage due to different production processes. Therefore, in order to facilitate advanced driver assistance in cities, map companies have begun to actively deploy the integrated production of SD maps and HD maps.

Baidu has developed SD-HD integrated AI map production platform, which integrates various data production structures and technological processes via a system. It satisfies the standardized and unified model expression of map data with different accuracy levels, thus solving the consistency problem.

For Here, different maps share the same map data, the same specification, and the same database. Here produces three modes of maps - SD, ADAS and HDML with the same standard, production environment and production process, so that they are interrelated by sharing and the same data and standard.

Strict supervision amid pilot application of HD maps

Six cities start HD map pilot application projects

In August, 2022, the General Office of the Ministry of Natural Resources of China issued the "Notice on HD Map Pilot Application Projects of Intelligent Connected Vehicles". The pilot projects will stage in six cities including Beijing, Shanghai, Guangzhou, Shenzhen, Hangzhou and Chongqing.

The Notice requires the provincial natural resources authorities in these pilot cities to work out pilot implementation plans, timetables and roadmaps in accordance with the deployment of the State Council and the national laws, regulations and policies on surveying, mapping, geographic information management and data security. Besides, they should rationally delineate the pilot scope according to the specific application scenarios of autonomous driving map data.

In August 2022, the Ministry of Transport of China issued "Opinions on HD map construction and other pilot projects of Beijing Baidu Netcom Science Technology Co., Ltd. for the purpose of building a transportation powerhouse". Baidu plans to provide centimeter-level HD map services on freeways and typical urban roads in three to five years, and make its integrated mobility service platform available in about 10 cities.

Strict supervision in HD map field

As China continues to strengthen the security management of geographic information, the Ministry of Natural Resources is intensifying the supervision over the HD map market while opening up pilot cities for application of intelligent connected vehicles.

Tightened supervision on surveying and mapping qualification: by the end of 2021, a total of 31 companies were approved for a-level electronic navigation map qualification, which is valid for 5 years, many enterprises need to re-apply for A-level electronic navigation map qualification in 2022, and there are 19 companies of A-level mapping qualification for navigation electronic map production that completed the re-examination and renewal in 2022.

Subjects of surveying and mapping: In August 2022, the Ministry of Natural Resources clearly pointed out that the following activities should be subject to the "Surveying and Mapping Law of the People's Republic of China": The intelligent connected vehicles installed or integrated with sensors such as satellite navigation and positioning modules, inertial measurement units, cameras and LiDAR collect, store, transmit and process spatial coordinates, images, point clouds, attributes and the like of vehicles and surrounding road facilities during operation, service and road testing.

China's first road HD electronic navigation map quality standard was officially established.

In September 2022, the Road HD Electronic Navigation Maps Quality Specification, recommended by Department of Land and Mapping of the Ministry of Natural Resources and led by Baidu, was officially approved by National Geographic Information Standardization Technical Committee, which is also the first industry standard for road HD electronic navigation map quality specification approved in China. It will solve "What to inspect, how to inspect, how to analyze and evaluate the inspection results, and how to compile the quality report", which are concerned by map and car companies.

“Global and China HD Industry Report, 2022” highlights the following:

  • Policies, regulations, standards and compliance about HD maps;
  • HD map market size, market structure, business models, etc.
  • HD map production technology, update technology, data distribution and fusion technology; the fusion application of HD maps and V2X; the application of HD maps in lane-level positioning, etc.
  • Main application scenarios of HD maps, such as autonomous passenger cars, automated parking of passenger cars, passenger and cargo transportation by autonomous driving, etc.;
  • HD map production and update technology, main products and application scenarios of major map companies at home and abroad;
  • HD map business layout and main technologies of major HD map technology providers at home and abroad.

Table of Contents

1 HD Map Policies, Standards and Regulations

  • 1.1 Policies Related to HD Maps
    • 1.1.1 Latest Policy in 2022: Carry Out the Pilot Application of HD Maps for Intelligent Connected Vehicles
    • 1.1.2 Latest Policy in 2022: Ministry of Transport Approves Baidu to Conduct HD Map Pilot
    • 1.1.3 Latest Policy in 2022: Shanghai Accelerates the Innovation and Development of Intelligent Connected Vehicles
  • 1.2 Regulations Related to HD Maps
    • 1.2.1 Foreign Regulations Related to HD Maps
    • 1.2.2 Domestic Regulations Related to HD Maps
    • 1.2.3 Latest Regulation in 2022: Review of Mapping Qualifications for HD Maps Gradually Tightened (1)
    • 1.2.4 Latest Regulation in 2022: Review of Mapping Qualifications for HD Maps Gradually Tightened (2)
    • 1.2.5 Latest Regulation in 2022: Ministry of Natural Resources Clarifies that Automotive Sensors and Intelligent Connected Vehicle Manufacturing are Illegal Mapping Activities (3)
    • 1.2.6 Latest Regulation in 2022: Chongqing Issued HD Map Management Measures for Intelligent Connected Vehicles (4)
  • 1.3 Standards Related to HD Maps
    • 1.3.1 Foreign Map Standard Ecology: Europe (1)
    • 1.3.2 Foreign Map Standard Ecology: Japan (2)
    • 1.3.3 Status Quo of Foreign HD Map Standard Development
    • 1.3.4 Status Quo of China HD Map Standard Customization (Published)
    • 1.3.5 Status Quo of China HD Map Standard Customization (Pre-research)
    • 1.3.6 Progress of China's HD Map Standard: Crowdsourcing Update Standard Gradually Improved
    • 1.3.7 Latest Standard in 2022: "General Specifications for Traffic Information Holographic Acquisition System of Road Intersections" Group Standard Officially Released (1)
    • 1.3.8 Latest Standard in 2022: First Domestic Road HD Electronic Navigation Maps Quality Specification Industry Standard Approved (2)
    • 1.3.9 Latest Standard in 2022: Domestic Autonomous Parking Map Standard Coming Soon (3)
    • 1.3.10 Latest Standard in 2022: First Provincial Local Standard Focusing on Intelligent Connected Vehicle Maps Launched (4)
  • 1.4 Compliance of HD Map
    • 1.4.1 State Increases Automotive Data Security Control
    • 1.4.2 HD Map Data Compliance Production Process
    • 1.4.3 HD Map Compliance Data Service
    • 1.4.4 HD Map Compliance Development Path: Building a HD Dynamic Map Basic Platform

2 HD Map Market Size and Competitive Pattern

  • 2.1 HD Map Market Size
    • 2.1.1 Estimated Installations of HD Maps in Autonomous Vehicles in China in 2022
    • 2.1.2 Market Size of OEM HD Maps for Passenger Cars in China (1)
    • 2.1.3 Market Size of OEM HD Maps for Passenger Cars in China (2)
  • 2.2 Competitive Landscape of HD Map Market
    • 2.2.1 HD Map Market Pattern
    • 2.2.2 HD Map Market Players (1): Top 10 Domestic Traditional Map Suppliers (1)
    • 2.2.3 HD Map Market Players (1): Top 10 Domestic Traditional Map Suppliers (2)
    • 2.2.4 HD Map Market Players (1): Product Comparison between Major Map Companies
    • 2.2.5 HD Map Market Players (I): Comparison between Three Major Map Producers
    • 2.2.6 HD Map Market Players (2): HD Map Layout of OEMs
    • 2.2.7 HD Map Market Players (2): OEMs Tend to Self-develop HD Maps Instead of Outsourcing
    • 2.2.8 HD Map Market Players (2): The OEMs That Develop Their Own HD Maps Are Facing Difficulties
    • 2.2.9 HD Map Market Players (2): HD Map Layout Cases of OEMs (1)
    • 2.2.10 HD Map Market Players (2): HD Map Layout Cases of OEMs (2)
    • 2.2.11 HD Map Market Players (3): Foreign Map Companies
  • 2.3 HD Map Business Models
    • 2.3.1 Scenarios where HD maps have been commercialized
    • 2.3.2 HD Map Business Model 1: Autonomous Driving
    • 2.3.3 HD Map Business Model 2: Parking Lots
    • 2.3.4 Classification of HD Map Profit Models
    • 2.3.5 Summary of HD Map Business Models: Domestic Map Companies (1)
    • 2.3.6 Summary of HD Map Business Models: Domestic Map Companies (2)
    • 2.3.7 Summary of HD Map Business Models: Foreign Map Companies (1)
    • 2.3.8 Summary of HD Map Business Models: Foreign Map Companies (2)
    • 2.3.9 Cases of HD Map Profit Models: Cloud SaaS Service Model

3 Key Technologies for HD Map Mass Production

  • 3.1 HD Map Production
    • 3.1.1 HD Map Production Flow
    • 3.1.2 HD Map Production Flow (I): Hard to Collect HD Maps of Urban Roads
    • 3.1.3 HD Map Production Flow (II): Point Cloud HD Map Generation
    • 3.1.4 HD Map Production Flow (II): Visual Fusion Point Cloud Mapping
    • 3.1.5 HD Map Production Flow (II): LIDAR Point Cloud Mapping
    • 3.1.6 HD Map Production Flow (III): Feature Extraction
    • 3.1.7 HD Map Production Tools: Open Source Architecture Lanelet2
    • 3.1.8 HD Map Production Tools: Open Source Architecture OpenDRIVE
    • 3.1.9 HD Map Production Cases (I): Baidu
    • 3.1.10 HD Map Production Cases (II): NavInfo's Automatic Map Production Line
    • 3.1.11 HD Map Production Cases (II): NavInfo's New Automated Mapping Technology
    • 3.1.12 Weak HD Map Production Flow
    • 3.1.13 Weak HD Map Production Flow (I): Spatial Fusion of Different Sensors
    • 3.1.14 Weak HD Map Production Flow (II): Environment Modeling
    • 3.1.15 Weak HD Map Production Flow (III): Realize Online HD Vector Map Reconstruction
    • 3.1.16 HD Map Production Technology Trend: SD/HD Map Integrated Production
  • 3.2 HD Map Update
    • 3.2.1 HD Map Update Trends: from Professional Collection to Crowdsourcing Update
    • 3.2.2 HD Map Crowdsourcing Update Method
    • 3.2.3 Challenges to HD Map Crowdsourcing Update (I)
    • 3.2.4 Challenges to HD Map Crowdsourcing Update (II)
    • 3.2.5 HD Map Crowdsourcing Update Solutions (I): Low-cost Collection + SLAM Algorithm
    • 3.2.6 HD Map Crowdsourcing Update Solutions (II): Vision-only + Deep Learning + SLAM Algorithm (1)
    • 3.2.7 HD Map Crowdsourcing Update Solutions (II): Vision-only + Deep Learning + SLAM Algorithm (2)
    • 3.2.8 HD Map Crowdsourcing Update Solutions (II): Vision-only + Deep Learning + SLAM Algorithm (3)
    • 3.2.9 HD Map Crowdsourcing Update Solutions (III): Cloud + Terminal Form An Update Closed Loop
    • 3.2.10 HD Map Update Subjects (I): OEM'S Crowdsourcing Update Solutions (1)
    • 3.2.11 HD Map Update Subjects (I): OEM'S Crowdsourcing Update Solutions (2)
    • 3.2.12 HD Map Update Subjects (II): Technology Providers' Crowdsourcing Update Solutions
    • 3.2.13 HD Map Crowdsourcing Update Cases (I): NavInfo's New Map Learning Platform (1)
    • 3.2.14 HD Map Crowdsourcing Update Cases (I): NavInfo's New Map Learning Platform (2)
    • 3.2.15 HD Map Crowdsourcing Update Cases (I): NavInfo's New Map Learning Platform (3)
    • 3.2.16 HD Map Crowdsourcing Update Cases (I): NavInfo's Proprietary Data Processing Platform
    • 3.2.17 HD Map Crowdsourcing Update Cases (II): TomTom Completes Crowdsourced Data Fusion over Cloud
  • 3.3 HD Map Data Distribution and Fusion
    • 3.3.1 HD Map Data Distribution and Fusion Process
    • 3.3.2 Process 1: HD Map Data Distribution Engine Architecture
    • 3.3.3 Process 1: Road Network Model of HD Map Data Distribution Engine
    • 3.3.4 Process 1: Integration Forms of HD Map Data Distribution Engine
    • 3.3.5 Process 1: HD Map Box in Integration Forms of HD Map Data Distribution Engine
    • 3.3.6 Process 1: Major HD Data Distribution Engine Providers
    • 3.3.7 Process 2: HD Map Data Format Conversion (1)
    • 3.3.8 Process 2: HD Map Data Format Conversion (2)
    • 3.3.9 Process 3: Interaction between HD Map Data Distribution and Receiving Terminals
    • 3.3.10 Process 4: HD Map Data Fusion
    • 3.3.11 Process 4: Integration of HD Map Data Distribution and ADAS
    • 3.3.12 Process 4: Relationship between HD Map Data Distribution and ADAS Applications
    • 3.3.13 HD Map Data Distribution and Fusion Cases (I): NavInfo's Cross-correlation Layer
    • 3.3.14 HD Map Data Distribution and Fusion Cases (II): Amap's Data Fusion and Application Methods
    • 3.3.15 HD Map Data Distribution and Fusion Trend: Centralized Processing in the Central Domain Controller
  • 3.4 Fusion Application of HD Map and V2X Technology
    • 3.4.1 Roles of HD Map in V2X (I): Infrastructure
    • 3.4.2 Roles of HD Map in V2X (II): Data Support
    • 3.4.3 Roles of HD Map in V2X (III): Helping to Achieve High-Precision Positioning
    • 3.4.4 Roles of V2X in HD Map (I): Data Storage
    • 3.4.5 Roles of V2X in HD Map (II): Data Distribution (1)
    • 3.4.6 Roles of V2X in HD Map (II): Data Distribution (2)
    • 3.4.7 Roles of V2X in HD Map (III): map Data Update
    • 3.4.8 HD Map and V2X Fusion Application Case: Traffic Lights Prompt
  • 3.5 HD Map Applied to Lane-Level Positioning
    • 3.5.1 Structure of HD Map Based Lane-Level Positioning Solutions
    • 3.5.2 Map Matching Technologies in Lane-Level Positioning Solutions (I): Map Matching Based on Point Cloud
    • 3.5.3 Map Matching Technologies in Lane-Level Positioning Solutions (I): Map Matching Algorithms Based on Point Cloud (1)
    • 3.5.4 Map Matching Technologies in Lane-Level Positioning Solutions (I): Map Matching Algorithms Based on Point Cloud (2)
    • 3.5.5 Map Matching Technologies in Lane-Level Positioning Solutions (II): Map Matching Based on Deep Learning (1)
    • 3.5.6 Map Matching Technologies in Lane-Level Positioning Solutions (II): Map Matching Based on Deep Learning (2)
    • 3.5.7 Providers of Lane-Level Positioning Solutions Based on HD Maps
    • 3.5.8 Application Cases (I): Mxnavi's Lane-Level Positioning Solutions Based on HD Maps
    • 3.5.9 Application Cases (II): Voyah's Urban Road HD Positioning Solutions Based on Weak HD Maps

4 Application Scenario for HD Map Mass Production

  • 4.1 HD Map Demand for Different Autonomous Driving Levels
    • 4.1.1 HD Map Road Element Demand for Different Autonomous Driving Level
    • 4.1.2 Requirements of Autonomous Driving for HD Map Elements: L2 NOA
    • 4.1.3 Requirements of Autonomous Driving for HD Map Elements: L2 Hands Free
    • 4.1.4 Requirements of Autonomous Driving for HD Map Elements: L3
    • 4.1.5 Requirements of Autonomous Driving for HD Map Elements: L4 (1)
    • 4.1.6 Requirements of Autonomous Driving for HD Map Elements: L4 (2)
    • 4.1.7 Autonomous Driving Development Stage : Human-car Co-Driving
    • 4.1.8 HD Map Framework in the Human-car Co-driving Stage
    • 4.1.9 Challenges of Human-car Co-driving Stage to HD Map Industry
  • 4.2 HD Map Application Scenario 1: Highway Autonomous Driving for Passenger Cars
    • 4.2.1 HD Map Installation of Independent Brand Mass-produced Passenger Cars (1)
    • 4.2.2 HD Map Installation of Independent Brand Mass-produced Passenger Cars (2)
    • 4.2.3 HD Map Installation of Independent Brand Mass-produced Passenger Cars (3)
    • 4.2.4 HD Map Installation of Independent Brand Mass-produced Passenger Cars (4)
    • 4.2.5 HD Map Installation of Independent Brand Mass-produced Passenger Cars (5)
    • 4.2.6 HD Map Installation of Joint Venture Brand Mass-produced Passenger Cars
    • 4.2.7 Mass Production Case 1: GAC Aion HD Map Installation Demand
    • 4.2.8 Mass Production Case 1: GAC Aion HD Map Solution
    • 4.2.9 Mass Production Case 1: GAC Aion EHP
    • 4.2.10 Mass Production Case 1: GAC Aion HD Map Curvature and Slope
    • 4.2.11 Mass Production Case 2: Xpeng HD Map Installation Solution
    • 4.2.12 Mass Production Case 2: Functions of XPeng P7 HD Map
    • 4.2.13 Mass Production Case 2: XPeng Enables Urban Assisted Driving Based on HD Map
    • 4.2.14 Mass Production Case 3: GWM WEY Realizes Point-to-Point Autonomous Driving with HD Map
    • 4.2.15 Mass Production Case 4: GM HD Map Installation Solution
    • 4.2.16 Mass Production Case 5: Li Auto HD Map Installation
    • 4.2.17 Mass Production Case 6: NIO Introduces HD Map from 2020
    • 4.2.18 Mass Production Case 6: NIO NOP Integrated with HD Map
    • 4.2.19 Mass Production Case 7: SAIC IM Autonomous Driving Hardware Solution
    • 4.2.20 Mass Production Case 8: SAIC MAXUS MIFA 9
    • 4.2.21 Mass Production Case 9: Geely ZEEKR 001
    • 4.2.22 Mass Production Case 10: AVATR 11 with Highway + Urban Scenario Assisted Driving
    • 4.2.23 Mass Production Plan 1: BMW HD map Installation Requirement and Solution (1)
    • 4.2.24 Mass Production Plan 1: BMW HD Map Installation Demand and Solution (2)
    • 4.2.25 Mass Production Plan 1: BMW HD Map Mass Production Function
    • 4.2.26 New Battlefield for Passenger Car Autonomous Driving: Urban Scenarios
    • 4.2.27 HD Map solution for Autonomous Driving in Urban Scenarios: SD Pro Map
    • 4.2.28 Urban Autonomous Driving Passenger Car HD Map Installation Solution 1: Multi-sensor Fusion + HD map
    • 4.2.29 Urban Autonomous Driving Passenger Car HD Map Installation Solution 2: Heavy on Perception + Light on Map
    • 4.2.30 Heavy on Perception + Light on Map Application Case 1: IDRIVERPLUS
    • 4.2.31 Heavy on Perception + Light on Map Application Case 2: Haomo AI
  • 4.3 HD Map Application Scenario 2: Low-speed Autonomous Parking for Passenger Cars
    • 4.3.1 AVP Map Type 1: HD Map
    • 4.3.2 AVP Map Type 1: SLAM Real-time map
    • 4.3.3 Top Five HD Map Suppliers for Parking Lots
    • 4.3.4 Application Case 1: Roadgrids Parking Lot HD Map Building System
    • 4.3.5 Application Case 2: ZongMu Technology HD Map-based Parking Product
    • 4.3.6 Autonomous Parking Map Development Trend: One Map of Vehicle / Field / Cloud / APP
  • 4.4 HD Map Application Scenario 3: Autonomous Driving for Cargo
    • 4.4.1 Importance of HD Maps for Low-speed Autonomous Driving
    • 4.4.2 HD Map Building Method for Low-speed Autonomous Driving: SLAM
    • 4.4.3 HD Map Supplier Pattern of Autonomous Driving for Cargo (1)
    • 4.4.4 HD Map Supplier Pattern of Autonomous Driving for Cargo (2)
    • 4.4.5 Application Case 1 of Autonomous Driving for Cargo: Meituan Autonomous Delivery Vehicle
    • 4.4.6 Application Case 1 of Autonomous Driving for Cargo: JD Autonomous Delivery Vehicle
  • 4.5 HD Map Application Scenario 4: Autonomous Driving for People
    • 4.5.1 HD Maps are a Must for High-level Autonomous Driving
    • 4.5.2 Application Case 1 of Autonomous Driving for People: Autonomous Robotaxi HD Map Application
    • 4.5.3 Application Case 1 of Autonomous Driving for People: Robobus HD Map Application
    • 4.5.4 Application Case of Autonomous Driving for People: PIX Moving Autonomous Minibus

5 Chinese and Foreign HD Map Providers

  • 5.1 Baidu Map
    • 5.1.1 "Vehicle-Road-Cloud-Map" Coordinated Development
    • 5.1.2 HD Map Product System
    • 5.1.3 Human-Computer Co-Driving Map
    • 5.1.4 Advantages of HD Map Products (I): SD/HD Map Integrated Production
    • 5.1.5 Advantages of HD Map Products (II): Strong Data Closed-Loop Update Capability
    • 5.1.6 Advantages of HD Map Products (III): High Update Frequency
    • 5.1.7 Advantages of HD Map Products (IV): Wide Coverage
    • 5.1.8 Advantages of HD Map Products (V): Verify Map Accuracy via Cockpit APP
    • 5.1.9 Advantages of HD Map Products (VI): Support Rapid Mass Production
    • 5.1.10 Advantages of HD Map Products (VII): Deep integration of Cockpit, Driving and Map
    • 5.1.11 HD Map Product Planning
    • 5.1.12 AVP HD Maps Are Collected in Real Time Using Machine Vision
    • 5.1.13 Integrate HD Map and Autonomous Driving (I)
    • 5.1.14 Integrate HD Map and Autonomous Driving (II)
    • 5.1.15 HD Map Ecosystem and Partners
  • 5.2 NavInfo
    • 5.2.1 Advantages in HD Map Market (I): Provide All-scenario HD Map Products
    • 5.2.2 Advantages in HD Map Market (II): Build Third-party HD Map Platforms
    • 5.2.3 In-depth Layout of Software and Hardware Integrated Solutions with HD Maps as the Core
    • 5.2.4 Build Barriers in the Key Link "Base Map-Update-Positioning"
    • 5.2.5 Major HD Map Products
    • 5.2.6 The HD Map Capability Has Exceeded L2+
    • 5.2.7 AVP Map
    • 5.2.8 HD Map Engine
    • 5.2.9 HD Map Update Technologies: UGC
    • 5.2.10 HD Map Update Technologies: Algorithms & Tools
    • 5.2.11 Data-Driven Open Platform for L5
    • 5.2.12 HD Map Quality Control System
    • 5.2.13 Partners Cover Automakers and Tier 1 Suppliers
  • 5.3 eMapgo
    • 5.3.1 Profile
    • 5.3.2 eMapgo and Luokung Technology Cooperated to Build A HD Map Platform Provider
    • 5.3.3 HD Map Products
    • 5.3.4 HD Map Update
    • 5.3.5 eHorizon (I)
    • 5.3.6 eHorizon (II)
    • 5.3.7 Parking Lot HD Map (I)
    • 5.3.8 Parking Lot HD Map (II)
    • 5.3.9 HD Map Cloud Platform
  • 5.4 Amap
    • 5.4.1 Profile
    • 5.4.2 Map Data Collection and Production
    • 5.4.3 Integrated Solutions Based on HD Map and High-Precision Positioning
    • 5.4.4 Third-generation Map Navigation for Vehicles
    • 5.4.5 HD Map and SD Map Matching
  • 5.5 Tencent
    • 5.5.1 Profile
    • 5.5.2 HD Map Solution
    • 5.5.3 HD Map Update
    • 5.5.4 Intelligent Driving Map for Human-Computer Co-Driving
    • 5.5.5 "Vehicle-Cloud Integration" Strategic Layout
  • 5.6 ECARX
    • 5.6.1 Financing
    • 5.6.2 HD Map Business
  • 5.7 BrightMap
    • 5.7.1 Profile
    • 5.7.2 Advantages of Parking Lot HD Map Products
    • 5.7.3 AVP HD Map
    • 5.7.4 AVP HD Map Data Delivery and Update
    • 5.7.5 Parking Navigation HD Map
    • 5.7.6 Secured Mass Production Orders for Parking Lot HD Maps
  • 5.8 Mxnavi
    • 5.8.1 Business Layout
    • 5.8.2 Full-link HD Map Service Capabilities
    • 5.8.3 HD Map Fusion Platform
    • 5.8.4 HD Map Crowdsourced Production Solution
  • 5.9 Huawei
    • 5.9.1 HD Map Layout
    • 5.9.2 Autonomous Driving Map Data System
    • 5.9.3 HD Map Cloud Services
    • 5.9.4 Huawei Accomplishes Crowdsourced Updates of HD Maps Based on the Open Autonomous Driving Platform
    • 5.9.5 HD Map Application: Holographic Intersections
    • 5.9.6 HD Map Application: Advanced Autonomous Driving System (ADS)
  • 5.10 Heading Data Intelligence
    • 5.10.1 Profile
    • 5.10.2 HD Map Business
    • 5.10.3 HD Map Updates
    • 5.10.4 HD Map Engines
    • 5.10.5 HD Map Application Scenarios: Parking
    • 5.10.6 HD Map Application Scenarios: Freeways/Urban Areas
    • 5.10.7 Products Based on HD Maps
    • 5.10.8 Listing and Trading of HD Electronic Map Data Products
  • 5.11 JD.com
    • 5.11.1 HD Map Business
    • 5.11.2 HD Map Application Scenarios
  • 5.12 SFMAP Technology
    • 5.12.1 HD Map Business
  • 5.13 Leador
    • 5.13.1 Profile
    • 5.13.2 Autonomous Driving Technology Based on HD Maps (1)
    • 5.13.3 Autonomous Driving Technology Based on HD Maps (2)
    • 5.13.4 Indoor Map Construction Based on SLAM Algorithms
    • 5.13.5 HD Maps for Parking Lots
    • 5.13.6 HD Map Application Scenarios
  • 5.14 Momenta
    • 5.14.1 Profile
    • 5.14.2 HD Map Technology Route
    • 5.14.3 The Role of HD Maps in Mpilot Parking
  • 5.15 HERE
    • 5.15.1 Profile
    • 5.15.2 Map Evolution Mode
    • 5.15.3 HD Map Business
  • 5.15. 4 HERE HD Live Map
    • 5.15.5 HD Map Data Updates
    • 5.15.6 HD Map Layout in China
    • 5.15.7 HERE Supports China's Automobile Brands to "Go Out"
  • 5.16 TomTom
    • 5.16.1 Profile
    • 5.16.2 HD Map Business
    • 5.16.3 TomTom AutoStream Delivery Service
    • 5.16.4 HD Map Collection and Drawing
    • 5.16.5 Crowdsourced Updates of HD Maps
    • 5.16.6 integrated ADAS Software Based on Maps
    • 5.16.7 Cooperation between TomTom and EB

6 HD Map-related Technology Companies

  • 6.1 Mobileye
    • 6.1.1 HD Map Business
    • 6.1.2 Map Data Coverage of Mobileye REM
    • 6.1.3 Mobileye REM Technology Realizes Crowdsourced Updates
    • 6.1.4 REM Reduces Map Production and Maintenance Cost
    • 6.1.5 Functions Achieved by Mobileye REM
    • 6.1.6 REM-based Map Extension Service of Mobileye
    • 6.1.7 Progress of REM in the World
    • 6.1.8 Mobileye Encounters Obstacles in the Chinese Market
    • 6.1.9 Mobileye Updates ZEEKR's Assisted Driving Functions via OTA
  • 6.2 Nvidia
    • 6.2.1 Nvidia acquired DeepMap to deploy HD maps
    • 6.2.2 Nvidia's DRIVE Map for Autonomous Vehicles (1)
    • 6.2.3 Nvidia's DRIVE Map for Autonomous Vehicles (2)
    • 6.2.4 DeepMap's Crowdsourced Update Solution (1)
    • 6.2.5 DeepMap's Crowdsourced Update Solution (2)
  • 6.3 Bosch
    • 6.3.1 Bosch Acquired Atlatec for HD Map Layout
    • 6.3.2 Bosch's Low-cost and Easy-to-deploy Mapping Solution
  • 6.4 DMP
    • 6.4.1 Profile
    • 6.4.2 DMP's Dynamic Map
  • 6.5 Carmera
    • 6.5.1 Toyota Acquired Carmera
    • 6.5.2 Carmera's Autonomous Driving 3D Map Solution
    • 6.5.3 Carmera's Map Data Collection Mode
  • 6.6 Kuandeng Technology
    • 6.6.1 Kuandeng Technology's HD Map Technology Solution
    • 6.6.2 Kuandeng Technology's HD Map Quality Evaluation System
  • 6.7 DeepMotion
    • 6.7.1 DeepMotion Was Acquired by Xiaomi
    • 6.7.2 DeepMotion's HD Map Technology Solution
    • 6.7.3 Features of DeepMotion's HD Maps
  • 6.8 Dilu Technology
    • 6.8.1 Profile
    • 6.8.2 HD map solutions
    • 6.8.3 Production Process of Dilu Technology's HD Maps (1)
    • 6.8.4 Production Process of Dilu Technology's HD Maps (2)
  • 6.9 Others
    • 6.9.1 HD Map Update Solutions of Horizon Robotics
    • 6.9.2 Mapbox's HD Map Services
    • 6.9.3 Mapper.ai