Global and China HD Map Industry Report, 2016

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Date: 09-Sep-2016
No. of pages: 110
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Global and China HD Map Industry Report, 2016 by ResearchInChina is mainly concerned with the following:


  • Acquisition modes and technical analysis of HD maps;

  • Market situation of global self-driving cars, covering structure and classification of autonomous driving, as well as domestic and foreign markets and policy environment;

  • Market situation of global HD maps, including status quo, layout, and development trends;

  • HD map industry chain, involvinglidar, cameras, positioning systems, IMU, and algorithms, etc.;

  • Analysis of 7 major Chinese and foreign HD map providers, containing technical analysis as well as development and future trends of HD map business.


At present, there are mainly four types of enterprises that dominate the layout in the HD map field: internet firms, auto makers, sensor vendors, and digital map providers.

I) Internet Firms

Internet tycoons like Google and UBER have, through acquisitions, obtained map data resources before producing HD maps based on their own algorithms and cloud computing capabilities. Google acquired a large number of digital map providers like Keyhole, Skybox, and Waze. In China, however, ground mapping is a highly confidential sector and therefore sets a higher entry threshold, which gives a big advantage to the Chinese digital map providers and turns away foreign players. Currently, there are 166 internet companies with mapping qualifications in China, and Internet giants like Baidu and Alibaba have through acquisitions occupied an important position in the Chinese map industry. Among them, Baidu purchased RITU and Alibaba bought AutoNavi.

II) Auto Makers

Car makers make their presence in HD maps mainly through M&As and self-building. For example, Toyota adopted onboard camera data to build HD maps via crowdsourcing, while Mercedes-Benz, Audi, and BMW jointly acquired HERE, a map provider under Nokia. Automakers-led layout could help promote the data traffic between HD maps and autonomous driving. Moreover, auto makers can fully open CAN bus port internally, which would bring benefits to the testing of HD map-based autonomous driving scheme.

III) Sensor Vendors

Using camera chips installed in cars, Mobileye is collecting data through crowdsourcing to make 3D maps. Its Road Experience Management software can identify specific road information like road markings. The bandwidth that it needs is only about 10KB per kilometer. Mobileye’s entry into HD map is to provide turnkey autonomous driving solutions for its future development.

IV) Digital Map Providers

Digital map providers, including foreign companies like Apple and TomTom and domestic ones such as Tencent, NavInfo, Xiaomi, and Careland, can complement each other's advantages at a minimum cost.

Overall, autonomous driving is now dominated by automakers, and they will not completely open their underlying data on vehicles to HD map providers. Thus, many auto makers (likeAudi, Mercedes-Benz, and BMW), obtain HD map data sources by acquiring digital map providers. In contrast, due to a lack of experience in fault tolerance and underlying data of vehicles, Internet companies end up cooperating with automakers.

Global and China HD Map Industry Report, 2016

Table of Contents

1 Overview of HD Map
1.1 Definition
1.2 Composition
1.3 Features
1.4 Merits
1.5 Classification

2 Map Acquisition Schemes
2.1 Acquisition Modes of General Map
2.1.1 Collection by Walking
2.1.2 Collection by Backpack
2.1.3 Collection by Bicycle
2.1.4 Other Collection Means
2.2 Acquisition Modes of HD Map
2.2.1 Collection by Special Vehicle
2.2.2 Collection by Crowdsourcing

3 Global HD Map Market
3.1 Overview of Autonomous Driving
3.1.1 Definition and Overview
3.1.2 Development Trend of Autonomous Driving in the World
3.2 Development of HD Map in the World and China
3.2.1 Global
3.2.2 China
3.3 Development Trend

4 Upstream Industry Chain of HD Map
4.1 Lidar
4.1.1 Operating Principle
4.1.2 Composition
4.1.3 Application in HD Map
4.1.4 Market Size
4.2 Camera
4.2.1 Operating Principle
4.2.2 Application in HD Map
4.2.3 Market Size
4.3 Positioning System
4.3.1 Operating Principle
4.3.2 Application in HD Map
4.4 Inertial Navigation System
4.4.1 Operating Principle
4.4.2 Application in HD Map
4.4.3 Market Size
4.5 Algorithms
4.5.1 Path Planning Algorithm
4.5.2 SLAM Algorithm

5 Major Foreign HD Map Providers
5.1 Google
5.1.1 Profile
5.1.2 Operation
5.1.3 Google’s Self-Driving Cars
5.1.4 HD Map Business
5.2 TomTom
5.2.1 Profile
5.2.2 Operation
5.2.3 HD Map Business
5.2.4 HD Map Acquisition Vehicle
5.3 HERE
5.3.1 Profile
5.3.2 Operation
5.3.3 HD Map Business
5.4 Mobileye
5.4.1 Profile
5.4.2 Operation
5.4.3 Products
5.4.4 HD Map Business

6 Key HD Map Providers in China
6.1 Baidu
6.1.1 Profile
6.1.2 Operation
6.1.3 Products
6.1.4 HD Map Business
6.2 AutoNavi
6.2.1 Profile
6.2.2 HD Map Business
6.3 NavInfo
6.3.1 Profile
6.3.2 Operation
6.3.3 Products
6.3.4 HD Map Business
6.3.5 Core Competitiveness

List of Charts

Map-Matching Function of HD Map
Composition Structure and Functions of HD Map
Features of HD Map
Difference between ADAS-level and HAD-level HD Maps
Way of Collection by Backpack
Backpack-mode Collection Equipment
Way of Collection by Bicycle
Interface of AutoNaviTaojin (Original Autonavigxdtaojin)
HERE’s HD Map Acquisition Vehicle
Mobileye’s HD Map Crowdsourcing Collection Scheme
Grades of Autonomous Driving
Development Stages (Predicted) of Autonomous Driving
Two Technology Roadmaps of Autonomous Driving System
Autonomous Driving Modes of Traditional Automakers, Parts Suppliers and Internet Firms
Autonomous Vehicle Development of Major Overseas Companies
Popularization (Predicted) of Autonomous Driving
Global HD Map Providers and Their Maps
Dominant Role of Internet Firms in HD Map Field
Automakers’ Presence in HD Map Field
Cooperative Modes of HD Map Providers
Chinese HD Map Providers and Their Maps
Future Evolution of HD Map
Composition of Lidar and Functions of Components
Emergence of Point Cloud Data
China Lidar Market Size, 2020E
Global In-vehicle Cameras Market Size, 2016-2020E
In-vehicle Camera OEM Market Size in China, 2016-2020E
Global Shipments of In-vehicle Cameras, 2016-2020
Parameters of Four Major Positioning Systems
Positioning Means of Four Major Positioning Systems (Civil Use)
Illustrative Diagram of GPS Point Positioning
Illustrative Diagram of Differential GPS
BDStar Navigation N280 Receiver and GPS-700 Series Antennas and Performance Parameters
Principle of Inertial Navigation System
Global Inertial Navigation System Market Size, 2016-2020E
Google’s Development History
Google Self-driving Car
Google’s Operation, 2011-2014
Google’s Tuned Prius
Google's Prototype Configuration
GoogleCar 3D Model
TomTom’s Services
TomTom’s Development History
TomTom’s Revenue, 2011-2015
TomTom’s Revenue by Business Segments, 2015
TomTom’s Revenue by Region, 2015
TomTomHD Map
ADAS Function of TomTom HD Map
Functions and Advantages of TomTom
Mapping Vehicle for TomTomHD Map
Data Processing Equipment for TomTom HD Map
TomTom’sRoadDNA Technology
HERE’s Revenue, 2013-2015
HERE HD Map
Characteristics of HERE HD Map
Composition Structure of HERE HD Map
Evolution of Mobileye Camera Sensor
Mobileye’s Solutions for Autonomous Driving
Mobileye’s Operation, 2011-2015
Mobileye’s Revenue by Market, 2013-2015
Mobileye’s Revenue by Region, 2015
Mobileye 560and Its Parameters
Functions of Mobileye 5 Series Products
Functions of Mobileye 5 Series Display Unit
Mobileye’s Smartphone App Interface
Functions of Various Generations of Mobileye Sensors
Mobileye’s Isomeric Architecture
Mobileye’s EyeQ5 Chip Architecture
Mobileye’s HD Map Technology (REM)
REM System Identification for Feasible Paths
Mobileye’sRoadbook Strategy
Layout of Baidu Telematics
Ecological Architecture of Baidu Telematics
Baidu’s Operation, 2011-2015
Main Functions of Baidu CarLife
Cooperative Auto Enterprises of CarLife
Baidu MyCar Architecture
Four Features of Baidu MyCar
Six Advantages of Baidu Speech System
Self-Driving Cars Developed by Baidu and BMW
Velodyne HDL-64E Structure Chart
VelodyneHDL-64E Parameters
HD Map Data Acquisition Vehicles Developed by Baidu and Changan
Velodyne HDL-32EStructure Chart
Velodyne HDL-32E Parameters
AutoNavi’s HD Map Production Plan
ADAS-level Acquisition Vehicle
HAD-level Acquisition Vehicle
VMX-450 Composition
Riegl VMX-450 System Architecture
VQ-450 Laser Scanner Structure
VQ-450 Performance Parameters
VMX-450-CS6 Camera
VMX-450-CS6 Parameters
Hierarchical Acquisition System of AutoNavi HD Map
NavInfo’s Navigation Electronic Map Product Diagram
NavInfo’s Vehicle Navigation Customers
NavInfo’s Operation, 2011-2015
Development of NavInfo’s Main Business, 2011-2015
NavInfo’s Layout in Telematics, 2011-2015
NavInfo’s Strategic Planning
NavInfo’sNavigation Electronic Map Compiling Products
Dynamic Traffic Information Service Diagram
WeDrive3.0 Eco-platform
NavInfoTraffic Index Platform
NavInfo’s Industry Application Products
NavInfo’s HD Map Diagram
NavInfo’sHD Map Test Vehicle
Typical Scenarios and Contents of HD Map
NavInfo’s HD Map Development Paths
NavInfo’s HD Map Solutions
NavInfo’s HD Map Data Specification
NavInfo’s HD Map and Location Sensing Product Series
NavInfo’s HD Map Product Roadmap
WeDrive 3.0 Cloud Service Platform for Autonomous Driving
NavInfo’s R&D Costs, 2011-2015

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