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市場調查報告書
商品編碼
1400761

自動駕駛地圖產業分析（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個工作天內

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簡介目錄

隨著高精地圖資質監管日趨嚴格，地圖採集成本、更新頻率、覆蓋面積等問題更加突出。在城市NOA（自動駕駛導航）熱潮中， "光圖" 式智慧駕駛解決方案正成為2023年的熱門話題。該解決方案減少了對離線高精地圖的依賴，並對高精地圖開發提出了挑戰。

自動駕駛的發展過程表明，人機協作將會存在一段時間。此階段所需的地圖不一定是高清地圖。整合不同地圖互補特性的多源地圖可能更適合現階段的自動駕駛需求。

組織和公司將如何因應新一代自動駕駛地圖的發展？

政府：在收緊高精地圖測繪A級資質的同時，加強ADAS地圖和B級測繪資質審核。

整車廠：由於相關部門對電子導航地圖測繪甲級資質的篩選更加嚴格，整車廠暫不引進測繪甲級資質。現在一些主機廠開始使用神經網路模型演算法進行即時地圖繪製，減少對離線高清地圖的依賴，Tesla、Li Auto、Xpeng、Huawei等支援ADS的車型就是典型的例子，這就是一個例子。

地圖提供者：為了滿足市場需求，我們推出了 "輕量級地圖" 解決方案，將標清資料、高清資料、LD資料等整合到一張地圖中，以確保導航的連續性。舉例來說，Tencent在宣布“三合一”智能駕駛地圖後，又推出了“智能駕駛雲地圖”，支持地圖提供商、汽車廠商、自動駕駛公司等企業協同建設。

新興汽車製造商主要活躍於 "光圖" 解決方案。原因之一是他們正在快速實施城市NOA能力，而高清地圖無法滿足他們的相關需求。

本報告分析了全球及中國自動駕駛地圖市場和產業，並提供了技術概述、相關法規和標準、技術和市場的最新狀況（保有量、滲透率、技術使用趨勢等） .)、未來趨勢等。我們整理並提供技術開發和利用場景、市場成長方向、主要公司簡介及其主要產品等資訊。

目錄

第一章 自動駕駛地圖政策、標準與法規現狀

第二章 自動駕駛地圖市場現狀

  • 自動駕駛地圖的發展方向
  • 自動駕駛地圖分類：導航地圖（SD地圖）
    • 汽車導航地圖：從2D更新為3D
    • 3D導航地圖佈局範例：騰訊
    • 導航地圖：為 "非地圖" 智慧駕駛解決方案提供基礎數據
    • 主流導航地圖：車內安裝現狀
    • 我國乘用車導航地圖安裝現況及安裝率
    • 中國乘用車導航地圖安裝現況及安裝率：依價格
    • 中國乘用車導航地圖安裝現況及安裝率：前20名車型
    • 中國乘用車導航地圖安裝現況及安裝率：20強品牌
  • 自動駕駛地圖分類：ADAS地圖（SD Pro MAP）
    • ADAS 地圖類別
    • ADAS地圖建立流程
    • ADAS地圖關鍵技術：基礎模型
    • ADAS地圖解決方案：主流地圖提供者預建地圖
    • ADAS 地圖解決方案：一些提供者使用演算法在線建立地圖
    • Tier 1 ADAS地圖解決方案：Baidu地圖技術的智慧駕駛解決方案
    • 第 1 層 ADAS 地圖解決方案：DeepRoute.ai 驅動程式 3.0
    • Tier 1 ADAS地圖解決方案：MAXIEYE的超空間架構
    • Tier 1 ADAS地圖解決方案：MAXIEYE的自動地圖記憶
    • Tier 1 ADAS地圖解決方案：Juefx Technology + Horizon Robotics
    • Tier 1 ADAS地圖解決方案：Huawei
    • Tier 1 ADAS地圖解決方案：Momenta非地圖智慧駕駛演算法解決方案
    • Tier 1 ADAS地圖解決方案：Momenta非地圖智慧駕駛演算法路線圖
    • ADAS地圖在車輛上的安裝狀況
    • OEM ADAS 地圖解決方案：Tesla FSD
    • OEM ADAS地圖解決方案：Voyah城市道路高精度定位解決方案
    • ADAS地圖發展趨勢：標清/高清地圖一體化製作
  • 自駕地圖分類: 高清地圖
    • 高畫質地圖
    • 透過感知與高精地圖的互補關係，提高城市NOA的安全性
    • 三大量產地圖提供者對比
    • OEM對高精地圖的態度
    • 高精地圖發展路線
  • 傳統地圖提供者如何根據城市 NOA 建立佈局？
    • 城市NOA將成為客車自動駕駛新戰場
    • 自動駕駛多源融合圖：有效解決城市NOA長期存在的問題
    • 城市NOA場景：地圖提供者重點實施SD Pro MAP
    • SD Pro MAP 的基本要求
    • Urban NOA 提倡的地圖提供者佈局思路：地圖創建和輕量級地圖模型
    • 地圖提供者佈局策略
  • OEM自動駕駛地圖選擇

第三章 高精地圖市場現狀

  • 高精地圖市場規模
    • 中國乘用車整車廠高清地圖市場規模
    • 相容於高精地圖的量產乘用車車型：中國銷量前十車型（2022-2023）
    • 國內配備高精度定位的量產乘用車車型價格區間（2022-2023年）
  • 高精地圖市場競爭格局
    • 高精地圖市場的主要參與者
    • 高精地圖市場的公司：中國地圖提供商
    • 高清地圖市場中的公司：OEM 高清地圖佈局
    • 高精地圖市場企業：OEM廠商自主開發高精地圖面臨挑戰
    • 解決挑戰的 OEM 解決方案
    • 高精地圖市場公司：國外地圖供應商
  • 引入高精地圖的商業模式
    • 高精地圖商業模式（一）：自動駕駛
    • 高精地圖商業模式（二）：停車場
    • 高精地圖獲利模式分類
    • 高精地圖商業模式概述：國內地圖提供商
    • 高精地圖商業模式概述：國外地圖供應商
    • 城市 NOA 發展中地圖提供者商業模式的變化
  • 開發高精地圖面臨的挑戰
    • 高精地圖發展面臨瓶頸
    • 高精地圖開發面臨的挑戰
  • 高精地圖資料分佈與融合
    • 高精地圖資料的分發與合併流程
    • 流程（一）：高精地圖資料分發引擎架構
    • 流程（一）：高精地圖資料分發引擎協作表單
    • 流程（一）：高精地圖資料分發引擎主要供應商
    • 流程（二）：高精地圖資料格式轉換
    • 流程（3）：高精地圖資料分發端與接收端的交互
    • 流程（4）：高精地圖資料融合
    • 高精地圖資料分佈與融合趨勢
  • 高清地圖應用於車道層級定位
    • 高清地圖車道層級定位解決方案：結構
    • 符合高清地圖的車道級定位解決方案：供應商
    • 案例分析

第四章 OEM智能駕駛地圖應用佈局

  • 不同等級自動駕駛所需的地圖元素
    • 自動駕駛所需的地圖要素：L2 NOA功能
    • 自動駕駛必備的地圖要素：L2級免持功能
    • 自動駕駛所需的地圖要素：L3
    • 自動駕駛所需的地圖要素：L4級以上
  • 主機廠將智慧駕駛地圖引進量產乘用車
    • 我國自主品牌量產乘用車智慧駕駛地圖安裝現狀
    • 合資品牌量產乘用車智慧駕駛地圖安裝狀況
    • OEM智慧駕駛地圖實施實例（一）：GAC Aion高畫質地圖解決方案
    • OEM智慧駕駛地圖實作實例（一）：GAC Aion電子地平線系統
    • OEM智慧駕駛地圖實作實例（一）：GAC Aion高畫質地圖的曲率與坡度
    • OEM智慧駕駛地圖介紹案例（二）：利用Xpeng實現符合高畫質地圖的城市NOA
    • OEM智慧駕駛地圖部署案例（二）：更新Xpeng XNGP "非地圖" 解決方案
    • OEM智慧駕駛地圖實施案例（三）：Great Wall WEY利用高畫質地圖實現P2P自動駕駛
    • OEM智慧駕駛地圖實施案例（四）：Li Auto高畫質地圖應用
    • OEM智慧駕駛地圖部署案例（四）：以Li AD Max 3.0更新 "非地圖" 解決方案
    • OEM智慧駕駛地圖實施案例（四）：Li Auto線上地圖技術的運用
    • OEM智慧駕駛地圖實現實例（五）：NIO NOP與高畫質地圖融合
    • OEM智慧駕駛地圖部署案例（五）：NIO深思 "非地圖" 解決方案
    • OEM智慧駕駛地圖實作實例（6）
    • OEM智慧駕駛地圖實現實例（7）
    • OEM智慧駕駛地圖實現實例（8）
    • OEM智慧駕駛地圖實現實例（9）
  • 智慧駕駛地圖使用現況：分場景-乘用車低速停車
    • AVP地圖類別（一）：高清地圖
    • AVP地圖類別（一）：SLAM即時地圖
    • 停車場停車地圖提供者：前 5 名的公司
    • 案例研究：如何繪製 Avatr 停車功能
  • 智慧駕駛地圖使用現況：分割場景-自動貨物運輸
    • 高精地圖在低速自動化貨物運輸的重要性
    • 低速自動物品運輸的高清測繪方法
    • 自動化貨物運輸高精地圖提供者模式
  • 智慧駕駛地圖使用現況：分場景-自動化人員輸送
    • 高清地圖在高度（自主）自動駕駛中的重要性
    • 自動化人員輸送使用場景

第五章 國內外地圖提供者

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

第六章 高精地圖科技企業

  • Mobileye
  • NVIDIA
  • DeepMotion
  • Mapbox
簡介目錄
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