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市场调查报告书
商品编码
1400761

自动驾驶地图产业分析（2024）

Autonomous Driving Map Industry Report,2024
出版日期: | 出版商: ResearchInChina | 英文 270 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

随着高精地图资质监管日趋严格，地图采集成本、更新频率、覆盖面积等问题更加突出。在城市NOA（自动驾驶导航）热潮中， "光图" 式智慧驾驶解决方案正成为2023年的热门话题。该解决方案减少了对离线高精地图的依赖，并对高精地图开发提出了挑战。

自动驾驶的发展过程表明，人机协作将会存在一段时间。此阶段所需的地图不一定是高清地图。整合不同地图互补特性的多源地图可能更适合现阶段的自动驾驶需求。

组织和公司将如何因应新一代自动驾驶地图的发展？

政府：在收紧高精地图测绘A级资质的同时，加强ADAS地图和B级测绘资质审核。

整车厂：由于相关部门对电子导航地图测绘甲级资质的筛选更加严格，整车厂暂不引进测绘甲级资质。现在一些主机厂开始使用神经网路模型演算法进行即时地图绘製，减少对离线高清地图的依赖，Tesla、Li Auto、Xpeng、Huawei等支援ADS的车型就是典型的例子，这就是一个例子。

地图提供者：为了满足市场需求，我们推出了 "轻量级地图" 解决方案，将标清资料、高清资料、LD资料等整合到一张地图中，以确保导航的连续性。举例来说，Tencent在宣布“三合一”智能驾驶地图后，又推出了“智能驾驶云地图”，支持地图提供商、汽车厂商、自动驾驶公司等企业协同建设。

新兴汽车製造商主要活跃于 "光图" 解决方案。原因之一是他们正在快速实施城市NOA能力，而高清地图无法满足他们的相关需求。

本报告分析了全球及中国自动驾驶地图市场和产业，并提供了技术概述、相关法规和标准、技术和市场的最新状况（保有量、渗透率、技术使用趋势等） .)、未来趋势等。我们整理并提供技术开发和利用场景、市场成长方向、主要公司简介及其主要产品等资讯。

目录

第一章 自动驾驶地图政策、标准与法规现状

第二章 自动驾驶地图市场现状

  • 自动驾驶地图的发展方向
  • 自动驾驶地图分类：导航地图（SD地图）
    • 汽车导航地图：从2D更新为3D
    • 3D导航地图布局范例：腾讯
    • 导航地图：为 "非地图" 智慧驾驶解决方案提供基础数据
    • 主流导航地图：车内安装现状
    • 我国乘用车导航地图安装现况及安装率
    • 中国乘用车导航地图安装现况及安装率：依价格
    • 中国乘用车导航地图安装现况及安装率：前20名车型
    • 中国乘用车导航地图安装现况及安装率：20强品牌
  • 自动驾驶地图分类：ADAS地图（SD Pro MAP）
    • ADAS 地图类别
    • ADAS地图建立流程
    • ADAS地图关键技术：基础模型
    • ADAS地图解决方案：主流地图提供者预建地图
    • ADAS 地图解决方案：一些提供者使用演算法在线建立地图
    • Tier 1 ADAS地图解决方案：Baidu地图技术的智慧驾驶解决方案
    • 第 1 层 ADAS 地图解决方案：DeepRoute.ai 驱动程式 3.0
    • Tier 1 ADAS地图解决方案：MAXIEYE的超空间架构
    • Tier 1 ADAS地图解决方案：MAXIEYE的自动地图记忆
    • Tier 1 ADAS地图解决方案：Juefx Technology + Horizon Robotics
    • Tier 1 ADAS地图解决方案：Huawei
    • Tier 1 ADAS地图解决方案：Momenta非地图智慧驾驶演算法解决方案
    • Tier 1 ADAS地图解决方案：Momenta非地图智慧驾驶演算法路线图
    • ADAS地图在车辆上的安装状况
    • OEM ADAS 地图解决方案：Tesla FSD
    • OEM ADAS地图解决方案：Voyah城市道路高精度定位解决方案
    • ADAS地图发展趋势：标清/高清地图一体化製作
  • 自驾地图分类: 高清地图
    • 高画质地图
    • 透过感知与高精地图的互补关係，提高城市NOA的安全性
    • 三大量产地图提供者对比
    • OEM对高精地图的态度
    • 高精地图发展路线
  • 传统地图提供者如何根据城市 NOA 建立布局？
    • 城市NOA将成为客车自动驾驶新战场
    • 自动驾驶多源融合图：有效解决城市NOA长期存在的问题
    • 城市NOA场景：地图提供者重点实施SD Pro MAP
    • SD Pro MAP 的基本要求
    • Urban NOA 提倡的地图提供者布局思路：地图创建和轻量级地图模型
    • 地图提供者布局策略
  • OEM自动驾驶地图选择

第三章 高精地图市场现状

  • 高精地图市场规模
    • 中国乘用车整车厂高清地图市场规模
    • 相容于高精地图的量产乘用车车型：中国销量前十车型（2022-2023）
    • 国内配备高精度定位的量产乘用车车型价格区间（2022-2023年）
  • 高精地图市场竞争格局
    • 高精地图市场的主要参与者
    • 高精地图市场的公司：中国地图提供商
    • 高清地图市场中的公司：OEM 高清地图布局
    • 高精地图市场企业：OEM厂商自主开发高精地图面临挑战
    • 解决挑战的 OEM 解决方案
    • 高精地图市场公司：国外地图供应商
  • 引入高精地图的商业模式
    • 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
    • 高精地图获利模式分类
    • 高精地图商业模式概述：国内地图提供商
    • 高精地图商业模式概述：国外地图供应商
    • 城市 NOA 发展中地图提供者商业模式的变化
  • 开发高精地图面临的挑战
    • 高精地图发展面临瓶颈
    • 高精地图开发面临的挑战
  • 高精地图资料分布与融合
    • 高精地图资料的分发与合併流程
    • 流程（一）：高精地图资料分发引擎架构
    • 流程（一）：高精地图资料分发引擎协作表单
    • 流程（一）：高精地图资料分发引擎主要供应商
    • 流程（二）：高精地图资料格式转换
    • 流程（3）：高精地图资料分发端与接收端的交互
    • 流程（4）：高精地图资料融合
    • 高精地图资料分布与融合趋势
  • 高清地图应用于车道层级定位
    • 高清地图车道层级定位解决方案：结构
    • 符合高清地图的车道级定位解决方案：供应商
    • 案例分析

第四章 OEM智能驾驶地图应用布局

  • 不同等级自动驾驶所需的地图元素
    • 自动驾驶所需的地图要素：L2 NOA功能
    • 自动驾驶必备的地图要素：L2级免持功能
    • 自动驾驶所需的地图要素：L3
    • 自动驾驶所需的地图要素：L4级以上
  • 主机厂将智慧驾驶地图引进量产乘用车
    • 我国自主品牌量产乘用车智慧驾驶地图安装现状
    • 合资品牌量产乘用车智慧驾驶地图安装状况
    • OEM智慧驾驶地图实施实例（一）：GAC Aion高画质地图解决方案
    • OEM智慧驾驶地图实作实例（一）：GAC Aion电子地平线系统
    • OEM智慧驾驶地图实作实例（一）：GAC Aion高画质地图的曲率与坡度
    • OEM智慧驾驶地图介绍案例（二）：利用Xpeng实现符合高画质地图的城市NOA
    • OEM智慧驾驶地图部署案例（二）：更新Xpeng XNGP "非地图" 解决方案
    • OEM智慧驾驶地图实施案例（三）：Great Wall WEY利用高画质地图实现P2P自动驾驶
    • OEM智慧驾驶地图实施案例（四）：Li Auto高画质地图应用
    • OEM智慧驾驶地图部署案例（四）：以Li AD Max 3.0更新 "非地图" 解决方案
    • OEM智慧驾驶地图实施案例（四）：Li Auto线上地图技术的运用
    • OEM智慧驾驶地图实现实例（五）：NIO NOP与高画质地图融合
    • OEM智慧驾驶地图部署案例（五）：NIO深思 "非地图" 解决方案
    • OEM智慧驾驶地图实作实例（6）
    • OEM智慧驾驶地图实现实例（7）
    • OEM智慧驾驶地图实现实例（8）
    • OEM智慧驾驶地图实现实例（9）
  • 智慧驾驶地图使用现况：分场景-乘用车低速停车
    • AVP地图类别（一）：高清地图
    • AVP地图类别（一）：SLAM即时地图
    • 停车场停车地图提供者：前 5 名的公司
    • 案例研究：如何绘製 Avatr 停车功能
  • 智慧驾驶地图使用现况：分割场景-自动货物运输
    • 高精地图在低速自动化货物运输的重要性
    • 低速自动物品运输的高清测绘方法
    • 自动化货物运输高精地图提供者模式
  • 智慧驾驶地图使用现况：分场景-自动化人员输送
    • 高清地图在高度（自主）自动驾驶中的重要性
    • 自动化人员输送使用场景

第五章 国内外地图提供者

  • Baidu Maps
  • NavInfo
  • Amap
  • Tencent
  • BrightMap
  • Mxnavi
  • Huawei
  • Heading Data Intelligence
  • JD
  • Leador
  • eMapgo
  • Momenta
  • Roadgrids
  • Here

第六章 高精地图科技企业

  • Mobileye
  • NVIDIA
  • DeepMotion
  • Mapbox
简介目录
Product Code: ZHP135

As the supervision of HD map qualifications tightens, issues such as map collection cost, update frequency, and coverage stand out. Amid the boom of urban NOA, the "lightweight map" intelligent driving solution has become a hot topic in 2023. This solution lessens the dependence on offline HD maps, posing a challenge to the development of HD maps.

From the development process of autonomous driving, it can be seen that human-machine co-driving will exist for a period of time. The need for maps in this phase is not necessarily HD maps. Multi-source maps that integrate the complementary characteristics of different maps may be more suitable for the needs of autonomous driving in this phase.

How do players respond to the development of new-generation autonomous driving maps?

Government: while tightening the Class A qualification for HD map surveying and mapping, work to enhance the review of ADAS maps and Class B surveying and mapping qualification.

In June 2023, the Map Technology Review Center of the Ministry of Natural Resources announced the phased progress in review of ADAS maps of ordinary urban roads across China, and allowed companies to submit ADAS maps of nationwide ordinary urban roads for review in batches. Currently, NavInfo's approved nationwide urban ADAS map data have covered 120 cities in 30 provinces; Baidu Maps has ADAS maps of 134 cities approved.

OEMs: relevant departments' stricter review of the Class A qualification for navigation electronic map surveying and mapping has discouraged OEMs to deploy the Class A qualification for map surveying and mapping. At present, some OEMs use neural network model algorithms for real-time mapping and lower reliance on offline HD maps, and the ADS-enabled models of Tesla, Li Auto, Xpeng, and Huawei are typical cases; some other OEMs prefer stability, and obtain surveying and mapping qualifications by way of applying for Class B qualification or establishing new joint ventures with map providers. For example, GAC together with its partners such as Nanjing Institute of Surveying, Mapping and Geotechnical Surveying Co., Ltd. co-funded "Guangdong Guangqi Yutu Equity Investment Partnership (Limited Partnership)"; Anhui NIO Smart Mobility Technology Co., Ltd., a subsidiary of NIO, applied for the Class A qualification for Internet map services.

Map providers: to meet the market demand, they launch "lightweight map" solutions, putting SD data, HD data, LD data, etc. on one map to ensure the continuity of navigation. One example is Tencent which introduced the "Intelligent Driving Cloud Map" to support the cooperative construction by map providers, automakers, autonomous driving companies and other players, after launching its "three-in-one" intelligent driving map.

Emerging carmakers take the lead in launching "lightweight map" solutions.

At present, OEMs' solutions that do not rely on HD maps don't mean that they do not use maps at all, but subtract elements from HD maps or add them to navigation maps instead.

It is mainly emerging carmakers that are more active in "lightweight map" solutions. One reason is that they implement urban NOA functions very quickly, and HD maps fail to answer their relevant needs.

Xpeng

In the first half of 2023, Xpeng started developing intelligent driving solutions based on SD maps. NGP that uses HD maps or does not use adopts the same technology stack. The only difference is that the original HD map input is replaced by the navigation map input, and the understanding of navigation information in real-time perception.

Xpeng's solution that does not use HD maps has the advantages of 4 to 10 times faster generalization speed, completely solving the problem of data freshness, reducing costs, and popularizing intelligent driving, compared with the solution using HD maps.

The "no offline HD map" solution implemented by Xpeng relies on XNet to build a "HD map" in real time.

Li Auto

Li Auto has launched urban NOA in 2023. This solution does not rely on HD maps. It aims to construct the features of intersections to assist in real-time perception and mapping. In a word, road sections are "unmapped", and intersections are mapped by crowdsourcing.

Li Auto is now promoting the NPN solution, hoping to solve the problem of online map updates.

In terms of OEMs' solutions, despite less dependence on HD maps, the "lightweight map" solution has higher requirements for vehicle perception and algorithms.

Conventional map providers launch lightweight autonomous driving map solutions to meet demand.

The voice of OEMs to "not rely on HD maps" is growing ever louder. To cater to the market demand, conventional map providers also make changes, trying hard to solve the three enduring problems of HD maps: update frequency, coverage area, and cost, and launching map products that more fit in with the current needs of autonomous driving.

Baidu

In July 2023, Baidu MapAuto 6.5, a human-machine co-driving map, was launched. It is a full 3D lane-level map and also an all-scenario human-machine co-driving map. It can provide three types of data: SD, LD and HD. Wherein, SD data has covered the whole country and is currently available on 10 million vehicles. Baidu's LD lightweight map data service consists of lane-level topology, complex scene geometry, experience layer, and dynamic information layer, allowing for daily update.

Amap

The new HQ Live MAP, launched in June 2023, combines the merits of HD MAP and SD MAP. In spite of a lower accuracy than HD MAP (absolute accuracy: 50cm, relative accuracy: 10cm), HQ Live MAP is enough for ADAS scenarios (highway and urban expressway scenarios: absolute accuracy of 1m, and relative accuracy of 30cm; ordinary urban road scenarios: relative accuracy of 1m), and it also simplifies unnecessary map elements in ordinary urban road scenarios, further reducing production and deployment costs.

Tencent

The latest Intelligent Driving Cloud Map, released in September 2023, enables fully cloud-based autonomous driving maps, supports element-level and minute-level online updates, and allows for the cooperative construction by map providers, automakers, autonomous driving companies and other players.

Tencent Intelligent Driving Cloud Map features scalable multi-layer forms, covering basic map layer, update element layer, ODD dynamic layer, driving experience layer and operation layer. Automakers can flexibly configure and manage the layers as they need, and build a data-driven operation platform suitable for themselves by combining it with their own data layer.

Autonomous Driving Map Industry Report,2024 highlights the following:

Autonomous driving map (formulation of policies, regulations, standards, etc.);

Vehicle map amid the development of urban NOA (development direction, coping strategies of conventional map providers, main types of maps used in urban NOA, etc.);

HD map (market status, market size, company pattern, business model, development challenges, etc.);

Application scenarios of intelligent driving map (high-speed autonomous driving of passenger cars, low-speed parking, autonomous human carrying, autonomous object carrying, etc.);

Major Chinese and foreign map providers (map product series, new product layout, product application cooperation, etc.);

HD map technology companies (technology layout, new technology R&D, etc.).

Table of Contents

1 Status Quo of Policies, Standards and Regulations Concerning Autonomous Driving Map

  • 1.1 Policies Concerning Autonomous Driving Map
    • 1.1.1 The Latest Policies in 2023: Guidelines for Construction of Intelligent Vehicle Basic Map Standard System (2023 Edition) (Released) (1)
    • 1.1.2 The Latest Policies in 2023: Guidelines for Construction of Intelligent Vehicle Basic Map Standard System (2023 Edition) (Released) (2)
    • 1.1.3 The Latest Policies in 2023: Guiding Opinions of Beijing Municipality on Piloting of HD Maps for Intelligent Connected Vehicles
    • 1.1.4 The Latest Policies in 2023: Administrative Regulations of Hangzhou City on HD Maps for Intelligent Connected Vehicles
  • 1.2 Regulations Concerning Autonomous Driving Map
    • 1.2.1 Foreign Regulations Concerning HD Map
    • 1.2.2 Chinese Regulations Concerning HD Map
    • 1.2.3 The Latest Regulations in 2023: National Regulatory Authorities Allow Maps of Nationwide City-level Roads to Be Submitted for Review
    • 1.2.4 The Latest Regulations in 2023: Improving the Efficiency of HD Map Review
  • 1.3 Standards Concerning Autonomous Driving Map
    • 1.3.1 Current Formulation of Foreign HD Map Standards
    • 1.3.2 Current Formulation of Chinese HD Map Standards (Released)
    • 1.3.3 Current Formulation of Chinese HD Map Standards (Pre-researched)
    • 1.3.4 Formulation of HD Map Standards in 2023: Incremental Update on Autonomous Driving Maps for Intelligent Connected Vehicles (Filed) (1)
    • 1.3.5 Formulation of HD Map Standards in 2023: Incremental Update on Autonomous Driving Maps for Intelligent Connected Vehicles (Filed) (2)

2 Status Quo of Autonomous Driving Map Market

  • 2.1 Development Direction of Autonomous Driving Maps
    • 2.1.1 Classification of Vehicle Maps: Navigation Map, ADAS Map and HD Map
    • 2.1.2 Autonomous Driving Is in the Phase of Human-machine Co-driving
    • 2.1.3 Challenges Posed to the Vehicle Map Industry in the Phase of Human-machine Co-driving
    • 2.1.4 Framework of Vehicle Map in the Phase of Human-machine Co-driving
    • 2.1.5 Vehicle Map Installation Trend: Navigation Map, ADAS Map and HD Map
  • 2.2 Classification of Autonomous Driving Maps: Navigation Map (SD Map)
    • 2.2.1 Vehicle Navigation Map Upgraded from 2D to 3D
    • 2.2.2 3D Navigation Map Layout Case: Tencent
    • 2.2.3 Navigation Map Provides Basic Data under the "Non-map" Intelligent Driving Solution (1)
    • 2.2.4 Navigation Map Provides Basic Data under the "Non-map" Intelligent Driving Solution (2)
    • 2.2.5 Installation of Mainstream Navigation Maps in Vehicles
    • 2.2.6 Installations and Installation Rate of Navigation Maps in Passenger Cars in China
    • 2.2.7 Installations and Installation Rate of Navigation Maps in Passenger Cars in China (by Price)
    • 2.2.8 Installations and Installation Rate of Navigation Maps in Passenger Cars in China (TOP20 Models)
    • 2.2.9 Installations and Installation Rate of Navigation Maps in Passenger Cars in China (TOP20 Brands)
  • 2.3 Classification of Autonomous Driving Maps: ADAS Map (SD Pro MAP)
    • 2.3.1 Categories of ADAS Maps
    • 2.3.2 ADAS Map Production Process
    • 2.3.3 ADAS Map Production Process 1
    • 2.3.4 ADAS Map Production Process 2
    • 2.3.5 ADAS Map Production Process 3
    • 2.3.6 Key Technology for ADAS Maps: Foundation Model
    • 2.3.7 ADAS Map Solution: Mainstream Map Providers Build Maps in Advance
    • 2.3.8 ADAS Map Solution: Some Providers Build Maps Online via Algorithms (1)
    • 2.3.9 ADAS Map Solution: Some Providers Build Maps Online via Algorithms (2)
    • 2.3.10 Tier1s' ADAS Map Solutions: Mapping Technology for Baidu Intelligent Driving Solution (1)
    • 2.3.11 Tier1s' ADAS Map Solutions: Mapping Technology for Baidu Intelligent Driving Solution (2)
    • 2.3.12 Tier1s' ADAS Map Solutions: DeepRoute.ai Driver 3.0 (1)
    • 2.3.13 Tier1s' ADAS Map Solutions: DeepRoute.ai Driver 3.0 (2)
    • 2.3.14 Tier1s' ADAS Map Solutions: MAXIEYE Hyperspace Architecture
    • 2.3.15 Tier1s' ADAS Map Solutions: MAXIEYE's Automatic Mapping Memory
    • 2.3.16 Tier1s' ADAS Map Solutions: Juefx Technology + Horizon Robotics
    • 2.3.17 Tier1s' ADAS Map Solutions: Huawei
    • 2.3.18 Tier1s' ADAS Map Solutions: Momenta's Non-map Intelligent Driving Algorithm Solution (1)
    • 2.3.19 Tier1s' ADAS Map Solutions: Momenta's Non-map Intelligent Driving Algorithm Solution (2)
    • 2.3.20 Tier1s' ADAS Map Solutions: Momenta's Non-map Intelligent Driving Algorithm Roadmap
    • 2.3.21 Installation of ADAS Maps in Vehicles (1)
    • 2.3.22 Installation of ADAS Maps in Vehicles (2)
    • 2.3.23 OEMs' ADAS Map Solutions: Tesla FSD (1)
    • 2.3.24 OEMs' ADAS Map Solutions: Tesla FSD (2)
    • 2.3.25 OEMs' ADAS Map Solutions: Voyah Urban Road High-Precision Positioning Solution
    • 2.3.26 Development Trend of ADAS Maps: Integrated Production of SD/HD Maps
  • 2.4 Classification of Autonomous Driving Maps: HD Map
    • 2.4.1 HD Map
    • 2.4.2 Perception and HD Maps Complement Each Other to Improve Urban NOA Safety
    • 2.4.3 Comparison between Three Major Mass-Production Map Providers
    • 2.4.4 OEMs' Attitude towards HD Maps
    • 2.4.5 HD Map Development Route
  • 2.5 How Do Conventional Map Providers Make Layout Driven by Urban NOA?
    • 2.5.1 Urban NOA Becomes A New Battlefield for Autonomous Driving of Passenger Cars
    • 2.5.2 Multi-source Fusion Map for Autonomous Driving Is An Effective Solution to Enduring Problems in Urban NOA
    • 2.5.3 In Urban NOA Scenario, Map Providers Focus on Deploying SD Pro MAP
    • 2.5.4 Basic Requirements for SD Pro MAP
    • 2.5.5 The Layout Idea of Map Providers Driven by Urban NOA: Create A Map and Lightweight Map Model
    • 2.5.6 Layout Strategy of Map Providers (1)
    • 2.5.7 Layout Strategy of Map Providers (2)
    • 2.5.8 Layout Strategy of Map Providers (3)
    • 2.5.9 Layout Strategy of Map Providers (4)
  • 2.6 Autonomous Driving Map Selection by OEMs
    • 2.6.1 Autonomous Driving Map Selection by OEMs (1)
    • 2.6.2 Autonomous Driving Map Selection by OEMs (2)

3 Status Quo of HD Map Market

  • 3.1 HD Map Market Size
    • 3.1.1 China's Passenger Car OEM HD Map Market Size (1)
    • 3.1.2 China's Passenger Car OEM HD Map Market Size (2)
    • 3.1.3 Top 10 HD Map-enabled Production Passenger Car Models by Sales in China, 2022-2023
    • 3.1.4 Price Range of Production Passenger Car Models with High-precision Positioning in China, 2022-2023
  • 3.2 Competitive Pattern of HD Map Market
    • 3.2.1 Major Players in HD Map Market
    • 3.2.2 Players in HD Map Market (1): Chinese Map Providers (1)
    • 3.2.3 Players in HD Map Market (1): Chinese Map Providers (2)
    • 3.2.4 Players in HD Map Market (2): HD Map Layout of OEMs
    • 3.2.5 Players in HD Map Market (2): OEMs Face Challenges in Self-development of HD Maps
    • 3.2.6 OEMs' Solutions to Map Challenges (1)
    • 3.2.7 OEMs' Solutions to Map Challenges (2)
    • 3.2.8 Players in HD Map Market (3): Foreign Map Providers
  • 3.3 Business Models for HD Map Implementation
    • 3.3.1 HD Map Business Model 1: Autonomous Driving
    • 3.3.2 HD Map Business Model 2: Parking Lot
    • 3.3.3 Classification of HD Map Profit Models
    • 3.3.4 Summary of HD Map Business Models: Chinese Map Providers (1)
    • 3.3.5 Summary of HD Map Business Models: Chinese Map Providers (2)
    • 3.3.6 Summary of HD Map Business Models: Foreign Map Providers
    • 3.3.7 Changes in Business Models of Map Providers in the Development of Urban NOA
  • 3.4 Challenges in Development of HD Maps
    • 3.4.1 Development of HD Maps Faces Bottlenecks
    • 3.4.2 Challenge 1 in Development of HD Maps
    • 3.4.3 Challenge 2 in Development of HD Maps
    • 3.4.4 Challenge 3 in Development of HD Maps
    • 3.4.5 Challenge 4 in Development of HD Maps
  • 3.5 HD Map Data Distribution and Fusion
    • 3.5.1 HD Map Data Distribution and Fusion Processes
    • 3.5.2 Process 1: HD Map Data Distribution Engine Architecture
    • 3.5.3 Process 1: HD Map data Distribution Engine Integration Form
    • 3.5.4 Process 1: Main Suppliers of HD Map Data Distribution Engine
    • 3.5.5 Process 2: HD Map Data Format Conversion (1)
    • 3.5.6 Process 2: HD Map Data Format Conversion (2)
    • 3.5.7 Process 3: Interaction between HD Map Data Distribution and Receiving End
    • 3.5.8 Process 4: HD Map Data Fusion
    • 3.5.9 HD Map Data Distribution and Fusion Trends
  • 3.6 HD Maps Applied to Lane-level Positioning
    • 3.6.1 Structure of Lane-level Positioning Solutions Based on HD Maps
    • 3.6.2 Providers of Lane-level Positioning Solutions Based on HD Maps
    • 3.6.3 Cases

4 Intelligent Driving Map Application Layout of OEMs

  • 4.1 Map Elements Required for Different Levels of Autonomous Driving
    • 4.1.1 Map Elements Required for Autonomous Driving: L2 NOA Function
    • 4.1.2 Map Elements Required for Autonomous Driving: L2 Hands Free Function
    • 4.1.3 Map Elements Required for Autonomous Driving: L3
    • 4.1.4 Map Elements Required for Autonomous Driving: L4 or Higher Level
  • 4.2 OEMs' Installation of Intelligent Driving Maps in Production Passenger Cars
    • 4.2.1 Chinese Independent Brands' Installation of Intelligent Driving Maps in Production Passenger Cars (1)
    • 4.2.2 Chinese Independent Brands' Installation of Intelligent Driving Maps in Production Passenger Cars (2)
    • 4.2.3 Chinese Independent Brands' Installation of Intelligent Driving Maps in Production Passenger Cars (3)
    • 4.2.4 Chinese Independent Brands' Installation of Intelligent Driving Maps in Production Passenger Cars (4)
    • 4.2.5 Chinese Independent Brands' Installation of Intelligent Driving Maps in Production Passenger Cars (5)
    • 4.2.6 Chinese Independent Brands' Installation of Intelligent Driving Maps in Production Passenger Cars (6)
    • 4.2.7 Chinese Independent Brands' Installation of Intelligent Driving Maps in Production Passenger Cars (7)
    • 4.2.8 Chinese Independent Brands' Installation of Intelligent Driving Maps in Production Passenger Cars (8)
    • 4.2.9 Joint Venture Brands' Installation of Intelligent Driving Maps in Production Passenger Cars
    • 4.2.10 OEMs' Intelligent Driving Map Installation Case 1: GAC Aion HD Map Solution
    • 4.2.11 OEMs' Intelligent Driving Map Installation Case 1: GAC Aion Electronic Horizon System
    • 4.2.12 OEMs' Intelligent Driving Map Installation Case 1: GAC Aion HD Map Curvature and Slope
    • 4.2.13 OEMs' Intelligent Driving Map Installation Case 2: Xpeng Realizes Urban NOA Based on HD Maps
    • 4.2.14 OEMs' Intelligent Driving Map Installation Case 2: Xpeng XNGP Upgrades "Non-map" Solution (1)
    • 4.2.15 OEMs' Intelligent Driving Map Installation Case 2: Xpeng XNGP Upgrades "Non-map" Solution (2)
    • 4.2.16 OEMs' Intelligent Driving Map Installation Case 2: Xpeng XNGP Upgrades "Non-map" Solution (3)
    • 4.2.17 OEMs' Intelligent Driving Map Installation Case 3: Great Wall WEY Uses HD Maps to Realize Point-to-point Autonomous Driving
    • 4.2.18 OEMs' Intelligent Driving Map Installation Case 4: Li Auto Uses HD Maps
    • 4.2.19 OEMs' Intelligent Driving Map Installation Case 4: Li AD Max 3.0 Upgrades "Non-map" Solution
    • 4.2.20 OEMs' Intelligent Driving Map Installation Case 4: Li Auto Uses Online Mapping Technology (1)
    • 4.2.21 OEMs' Intelligent Driving Map Installation Case 4: Li Auto Uses Online Mapping Technology (2)
    • 4.2.22 OEMs' Intelligent Driving Map Installation Case 5: NIO NOP Fuses HD Maps
    • 4.2.23 OEMs' Intelligent Driving Map Installation Case 5: NIO Carefully Explores "Non-map" Solution
    • 4.2.24 OEMs' Intelligent Driving Map Installation Case 6
    • 4.2.25 OEMs' Intelligent Driving Map Installation Case 7
    • 4.2.26 OEMs' Intelligent Driving Map Installation Case 8
    • 4.2.27 OEMs' Intelligent Driving Map Installation Case 9
  • 4.3 Intelligent Driving Map Application in Sub-scenarios: Low-speed Parking of Passenger Cars
    • 4.3.1 AVP Map Category 1: HD Map
    • 4.3.2 AVP Map Category 1: SLAM Real-Time Map
    • 4.3.3 Top Five Providers of Parking Maps for Parking Lots
    • 4.3.4 Installation Case: Mapping Method for Avatr Parking Functions
  • 4.4 Intelligent Driving Map Application in Sub-scenarios: Autonomous Object Carrying
    • 4.4.1 Importance of HD Maps for Low-speed Autonomous Object Carrying
    • 4.4.2 HD Mapping Method for Low-speed Autonomous Object Carrying
    • 4.4.3 Pattern of Providers of HD Maps for Autonomous Object Carrying (1)
    • 4.4.4 Pattern of Providers of HD Maps for Autonomous Object Carrying (2)
  • 4.5 Intelligent Driving Map Application in Sub-scenarios: Autonomous Human Carrying
    • 4.5.1 Importance of HD Maps for High-level (Autonomous) Automated Driving
    • 4.5.2 Application Scenarios of Autonomous Human Carrying (1)
    • 4.5.3 Application Scenarios of Autonomous Human Carrying (2)
    • 4.5.4 Application Scenarios of Autonomous Human Carrying (3)

5 Chinese and Foreign Map Providers

  • 5.1 Baidu Maps
    • 5.1.1 Autonomous Driving Architecture Adjustment: Constrict L4/L2 Solutions
    • 5.1.2 Baidu Is Committed to Building Maps for Autonomous Driving
    • 5.1.3 Vehicle Map Product System
    • 5.1.4 Vehicle Map Product 1: Navigation Map
    • 5.1.5 Vehicle Map Product 2: Baidu MapAuto 6.5 (1)
    • 5.1.6 Vehicle Map Product 2: Baidu MapAuto 6.5 (2)
    • 5.1.7 Vehicle Map Product 2: Baidu MapAuto 6.5 (3)
    • 5.1.8 Vehicle Map Product 3: HD Map (1)
    • 5.1.9 Vehicle Map Product 3: HD Map (2)
    • 5.1.10 Map Is A Competitive Edge of Baidu's Autonomous Driving System
    • 5.1.11 Core Value 1 of "Familiar Road" Map: Safety (1)
    • 5.1.12 Core Value 1 of "Familiar Road" Map: Safety (2)
    • 5.1.13 Core Value 2 of "Familiar Road" Map: Comfort
    • 5.1.14 Core Value 3 of "Familiar Road" Map: High Efficiency
    • 5.1.15 Low-cost Construction of Intelligent Driving Map Technology 1: Mapping
    • 5.1.16 Low-cost Construction of Intelligent Driving Map Technology 2: Automatic Feature Extraction
    • 5.1.17 Compared with HD Maps, Baidu Autonomous Driving Map Loses Weight
  • 5.2 NavInfo
    • 5.2.1 New Vehicle Map Product System
    • 5.2.2 New Vehicle Map Product 1: Navigation Map
    • 5.2.3 New Vehicle Map Product 2: Scene map (1)
    • 5.2.4 New Vehicle Map Product 2: Scene Map (2)
    • 5.2.5 New Vehicle Map Product 3: HD Map (1)
    • 5.2.6 New Vehicle Map Product 3: HD Map (2)
    • 5.2.7 New Vehicle Map Product 3: HD Map (3)
    • 5.2.8 New Vehicle Map Product 3: HD Map (4)
    • 5.2.9 Intelligent Driving Map Application Case 1
    • 5.2.10 Intelligent Driving Map Application Case 2
    • 5.2.11 Intelligent Driving Map Application Case 3
  • 5.3 Amap
    • 5.3.1 Vehicle Map Product 1
    • 5.3.2 Vehicle Map Product 2
    • 5.3.3 Vehicle Map Product 3
    • 5.3.4 Matching of HD Map and SD Map
  • 5.4 Tencent
    • 5.4.1 "Vehicle-Cloud Integration" Strategic Layout
    • 5.4.2 Vehicle Map Product 1: Navigation Map
    • 5.4.3 Vehicle Map Product 2: Intelligent Driving Cloud Map (1)
    • 5.4.4 Vehicle Map Product 2: Intelligent Driving Cloud Map (2)
    • 5.4.5 Vehicle Map Product 3
    • 5.4.6 Vehicle Map Product 4
    • 5.4.7 Coping Strategies in "Lightweight Map" Mode: In-depth Cooperation with Tier1s (1)
    • 5.4.8 Coping Strategies in "Lightweight Map" Mode: In-depth Cooperation with Tier1s (2)
  • 5.5 BrightMap
    • 5.5.1 Introduction to Vehicle Map Business
    • 5.5.2 Vehicle Map Product: AVP HD Map (1)
    • 5.5.3 Vehicle Map Product: AVP HD Map (2)
  • 5.6 Mxnavi
    • 5.6.1 Business Layout
    • 5.6.2 Vehicle Map Product 1: Crowdsourced Map Technology
    • 5.6.3 Vehicle Map Product 2: HD Map Data
    • 5.6.4 Vehicle Map Product 3: HD Map Fusion Platform
    • 5.6.5 Coping Strategies in "Lightweight Map" Mode
  • 5.7 Huawei
    • 5.7.1 Vehicle Map Products (1)
    • 5.7.2 Vehicle Map Products (2)
    • 5.7.3 Vehicle Map Products (3)
    • 5.7.4 Vehicle Map Application: High-level Autonomous Driving System (ADS)
  • 5.8 Heading Data Intelligence
    • 5.8.1 Map-based Product Lines
    • 5.8.2 Vehicle Map Products (1)
    • 5.8.3 Vehicle Map Products (2)
    • 5.8.4 HD Map Application Scenario 1: Parking
    • 5.8.5 HD Map Application Scenario 2: Highway/Urban Driving Assistance
  • 5.9 JD
    • 5.9.1 JD Logistics Builds "Yutu" Platform (1)
    • 5.9.2 JD Logistics Builds "Yutu" Platform (2)
  • 5.10 Leador
    • 5.10.1 Autonomous Driving Technology Based on HD Maps
    • 5.10.2 Application of HD Map in Parking Lots
  • 5.11 eMapgo
    • 5.11.1 Vehicle Map Products: HD Map for Parking Lots (1)
    • 5.11.2 Vehicle Map Products: HD Map for Parking Lots (2)
    • 5.11.3 Vehicle Map Products: HD Map Cloud Platform
    • 5.11.4 Vehicle Map Application: Autonomous Driving Simulation Test
  • 5.12 Momenta
    • 5.12.1 Coping Strategies in "Lightweight Map" Mode
    • 5.12.2 Non-map Solution Algorithm: Lane Line Recognition
    • 5.12.3 Non-map Solution Algorithm: Positioning
    • 5.12.4 Non-map Solution Algorithm: Planning & Control
    • 5.12.5 Algorithm Iteration Path
  • 5.13 Roadgrids
    • 5.13.1 Automatic HD Map Building and Update
    • 5.13.2 Selection of Lightweight HD Map Elements
    • 5.13.3 Lightweight Map Closed-loop Solution (1)
    • 5.13.4 Lightweight Map Closed-loop Solution (2)
  • 5.14 Here
    • 5.14.1 Map Evolution Mode
    • 5.14.2 Emphasize Map Information Security
    • 5.14.3 Launch UniMap Mapping Platform
    • 5.14.4 HD Map Layout in China

6 HD Map Technology Companies

  • 6.1 Mobileye
    • 6.1.1 Focus on Deploying Lightweight Map Business (1)
    • 6.1.2 Focus on Deploying Lightweight Map Business (2)
    • 6.1.3 Benefits of REM
  • 6.2 NVIDIA
    • 6.2.1 Vehicle Map Business: DeepMap
    • 6.2.2 Vehicle Map Product: DRIVE Map (1)
    • 6.2.3 Vehicle Map Product: DRIVE Map (2)
  • 6.3 DeepMotion
    • 6.3.1 Acquired by Xiaomi
    • 6.3.2 HD Map Technical Solution
    • 6.3.3 Features of HD Map
  • 6.4 Mapbox
    • 6.4.1 Vehicle Map Products: Navigation Map
    • 6.4.2 Vehicle Map Products: HD Map
    • 6.4.3 Failure in the Chinese Market