HD Map for Autonomous Vehicles Market by Solution (Cloud-Based & Embedded), LOA(L2, L3, L4, & L5), Usage (Passenger & Commercial), Vehicle Type, Services (Advertisement, Mapping, Localization, Update & Maintenance), & Region - Global Forecast to 2030
[288 Pages Report] The HD Map for Autonomous Vehicles Market, by value, is estimated to be USD 1.4 billion in 2021 and is projected to reach USD 16.9 billion by 2030, at a CAGR of 31.7% from 2021 to 2030. HD maps are crucial for the development of autonomous vehicles. HD maps are built and updated in real-time by using the data captured by various sensors, cameras, LiDAR. This data helps autonomous vehicles to make better decisions while performing driving tasks. HD maps can also provide localization information, which will help find the vehicle's accurate position on the map. With recent advancements in computer vision and artificial intelligence, visual Simultaneous Localization and Mapping (SLAM) has gained significant accuracy in determining the position and orientation of a device with respect to its localization while simultaneously mapping the environment around that device. It is expected that HD maps will also provide advertising services, which will be the key revenue-generating segment for HD maps companies.
The key strategy followed by autonomous vehicle developers is in-house mapping. Companies such as Cruise, Waymo, and Uber are developing their HD maps. These companies are using their vehicles that are equipped with various sensors to map the data across cities. For example, Cruise is developing an HD map of San Francisco using precision LiDAR and semantic mapping techniques. The advantages of in-house HD mapping are control over the maintenance strategy, adding features and information according to the requirement, maintaining accuracy, and being cost-effective. Hence, the adoption of an in-house mapping strategy by the key autonomous vehicle developers can impact the revenues of other key players such as TomTom, HERE technologies, and NVIDIA.
North America is estimated to be the largest market for HD maps for autonomous vehicles during the forecast period. The North American market is principally driven by the increasing demand for a safe, efficient, and convenient driving experience; rising investment in autonomous vehicle technology; and a strong presence of HD map suppliers. The increase in government support and the availability of suitable infrastructure for semi-autonomous and autonomous vehicles are likely to drive market growth in the region.
Some of the major players in the HD map for autonomous vehicle market are TomTom (the Netherlands), HERE Technologies (the Netherlands), Waymo (US), NVIDIA (US), Baidu (China), Dynamic Map Platform (Japan), NavInfo (China), and Zenrin (US). These players have long-term supply contracts with leading automotive manufacturers and autonomous vehicle technology developers. These companies have adopted the strategies of new product developments, acquisitions, agreements, collaborations, expansions, joint ventures, partnerships, and supply contracts to gain traction in the HD map for autonomous vehicle market. Partnership and collaboration are the most widely adopted strategies by major players.
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COVID-19 Impact on HD map for autonomous vehicles Market:
The COVID-19 pandemic has suspended production, plunged sales, and has compelled key players in the global automotive field to modify or change their strategies. Rescheduling the launch of models and projects, stabilizing dealer networks, managing cash, and reviewing investment portfolios have affected the production and sales of passenger and commercial vehicles across the globe. The development, testing, and deployment of autonomous vehicles and the growth of the HD map space are expected to slow down as well. Due to the COVID-19 pandemic, most of the countries imposed complete lockdowns for more than two months, which, in turn, impacted vehicle production. Manufacturing units around the world were shut down, and vehicle sales were significantly affected.
Driver: Growing trend of autonomous driving
There has been an increasing interest in autonomous vehicles throughout the world. Major players in the autonomous driving space are focusing on the research and development of autonomous driving technology. Leading OEMs such as Ford, Volkswagen, Mercedes, Toyota, Audi, General Motors, Honda, and Tesla have already rolled out plans for the development of autonomous vehicles. For instance, Audi plans to invest approximately USD 16 billion by 2023 on self-driving and sustainable technology. Major advantages of autonomous vehicles, such as increased efficiency, elimination of human errors in driving, environmentally friendly operation, etc., are driving the growth of autonomous vehicles. With the increasing population in urban areas, traffic congestion has evolved as a major concern. Autonomous vehicles can maintain shorter minimum distances between two vehicles compared to human-driven vehicles, which results in better traffic flow, reduction in emission of gases, and reduced congestion. In the Asia Pacific region, China is considered the most favorable country for the adoption of autonomous vehicles. The factors such as government support and investments by autonomous OEMs and technology companies such as Baidu and DiDi for the development of autonomous vehicles are expected to drive the growth of the market in China. HD maps market is directly dependent on the growth of the autonomous vehicles market. HD maps used in autonomous vehicles will ensure accurate navigation and safe commute, which will further boost the demand for HD maps.
Restraint: Limited standardization in HD maps
The standardization of HD maps is a crucial element in the development of HD maps technology. At present, there is no single authoritative source or database of HD mapping data. With technological advancement, the amount of map information is increasing rapidly. The standardization and storage of this data have emerged as major concerns. Despite the existing standards such as ADASIS, NDS, SENSORIS, and TISA, the database still lacks interoperability between the mapping suppliers. At the early stages, new players are introducing their solutions, which is expected to lead to rapid innovation and accelerate the adoption of crowdsourced map technology. However, in the long-term perspective, economies of scale and a maturing market environment will require the adoption of standards and open-platform approaches. The lack of a single automotive-grade navigation base is one of the crucial obstacles to the full commercial readiness and safety of self-driving cars. Individual smart car manufacturers, as well as navigation technology companies such as TomTom, HERE Maps, or Nvidia, are publishing their own maps in proprietary formats; however, the real value of HD mapping can only be realized through standardization. Without it, map integrity and reliability are much more difficult to achieve. In fact, the NDS (Navigation Data Standards) Association was founded in recognition of the need for consistent data specifications for HD mapping. However, many government bodies, companies, and associations are working on the standardization of HD maps. For instance, the UK is working toward the standardization of HD maps for autonomous vehicles. A new report by Zenzic, the UK hub organization for self-driving vehicle development, and Ordnance Survey (OS), Great Britain’s National Mapping Agency, laid down global standards for HD-mapping in 2019.
Opportunity: Autonomous renting car services
The rapid development in automotive technology is moving the world towards connected, shared, electric, and ultimately autonomous vehicle. In the near future, autonomous vehicles are expected to play a key role in car renting services as, with advancements in technology, consumers will trust autonomous cars as a safe mode of transport. In 2018, leading autonomous driving technology development company Waymo has partnered with self-driving rental car company Avis Budget Group, which owns about 580,000 vehicles in 11,000 locations in 180 countries. Moreover, in 2017, Rent-A-Car, the largest rental car service operator in the US, signed an agreement with autonomous driving technology startup Voyage to manage its fleet of autonomous cars. As per the agreement, the car rental company will handle the maintenance of the autonomous vehicle fleet. Autonomous car renting is expected to reduce per-mile cost, which is expected to be a significant reason for the growth of these vehicles. With such developments in America, autonomous car renting service is expected to hit the roads within a span of 5 to 7 years. This is a significant opportunity for the HD maps market. Also, in the Asia Pacific region leading transportation network companies such as Ola and Uber are investing in autonomous vehicle technology, which is expected to boost the HD maps market.
Challenge: Legal and privacy issues regarding HD maps
Legal and privacy issues are an important challenge in the development of HD maps. Legal issues are restricting the data collection process, location privacy, licensing, intellectual property rights, use of geospatial information, and storage of geospatial data. There are various laws and regulations pertaining to mapping technology, which vary according to the geographic location. There are countries that forbid the mapping of geospatial data. For instance, in May 2016, the Ministry of Home Affairs of the Government of India passed the Geospatial Information Regulation Bill. The objective of this bill is to regulate the acquisition, publication, and distribution of geospatial information of India, which affects the security, sovereignty, and integrity of the country. Such regulations are formulated with respect to national security interests. Most of the data captured for HD mapping is with the help of LiDARs and cameras, which continuously scan the streets while driving. In such cases adhering to regulations regarding people’s privacy is considered a tough task. Hence a clear stated policy for HD mapping is required across the globe.
Personal mobility account for a major share of the market during the forecast period
The HD map for autonomous vehicle market for the commercial mobility segment is projected to grow fastest during the forecast period. Ride-sharing, robo-taxi, goods transportation, and other commercial activities are considered in the commercial mobility segment. The introduction of robo-taxis is expected to present new challenges regarding car ownership or mobility as a service model. Car companies producing generic, as well as robo-taxis, are adopting advanced production technologies. In robo-taxis, testing is higher for cars rather than large transport vehicles. Waymo was one of the first companies to charge people for rides in self-driving Waymo One, which had human backup drivers. Chrysler has supplied thousands of Chrysler Pacifica minivans to Waymo. The collaboration is expected to open a new business model for companies developing or working on robo-taxis. AutoX and Optimus Ride are some of the companies offering autonomous cars as robo-taxis.
The Asia Ocenia is projected to be the fastest-growing HD map for autonomous vehicles market, by 2030
Asia Oceania region is projected to be the fastest-growing market for HD map for autonomous vehicle during the forecast period. The factors that could contribute to the growth of the region are the presence of leading HD map providers such as AutoNavi, NavInfo, and Momenta and the changing regulations concerned with autonomous driving.
Asia Oceania region comprises countries such as China, Japan, India, and South Korea. In recent years, China has emerged as a hub for automobile production. Infrastructural developments and industrialization activities in emerging economies have opened new avenues, creating several opportunities for automotive OEMs. This has triggered the growth of the HD mapping market in these regions. MapmyIndia is the leading HD mapping solution provider for the Indian market, which serves top companies such as BMW, Ford, Hyundai, etc. The growing technological advancements in China have increased the demand for semi-autonomous vehicles, which is going to increase the market for HD maps. The economic growth in this region has increased the purchasing power of the public. Increasing government support and investments in autonomous vehicle technology boost the growth of the HD Map for the autonomous vehicle market in the Asia Oceania region. The partnerships of the top HD mapping companies in this region such as NavInfo and Momenta with leading companies like HERE, Ambarella, and Analog Devices can also boost the growth of the HD map market in Asia Oceania.
Key Market Players
The HD map for autonomous vehicles market includes players such as TomTom (the Netherlands), HERE Technologies (the Netherlands), Waymo (US), NVIDIA (US), Baidu (China), Dynamic Map Platform (Japan), NavInfo (China), and Zenrin (US). These players have long-term supply contracts with leading automotive manufacturers and autonomous vehicle technology developers. These companies have adopted the strategies of new product developments, acquisitions, agreements, collaborations, expansions, joint ventures, partnerships, and supply contracts to gain traction in the HD map for autonomous vehicle market. Partnership and collaboration are the most widely adopted strategies by major players.
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Scope of the Report
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|
Report Metric |
Details |
|
Market size available for years |
2020–2030 |
|
Base year considered |
2021 |
|
Forecast period |
2021-2030 |
|
Forecast Market Size |
Value (USD Million) |
|
Segments covered |
Of service type (mapping, localization, updates and maintenance, and advertisement), level of automation (semi-autonomous driving vehicles, autonomous driving vehicles), solution type (cloud based, embedded), usage type (operational data, commercial mobility), vehicle type (passenger car, commercial vehicles) & region |
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Region covered |
Asia Pacific, North America, Europe, and the RoW |
|
Companies Covered |
TomTom (Netherlands), HERE (Netherlands), NVIDIA (US), Waymo (US), Baidu (China), Dynamic Map Platform (Japan), and NavInfo (China). |
This research report categorizes the HD map for autonomous vehicles market based on train type, application, component, cable type, voltage, material type, wire length, end use and region
By Service Type
- Mapping,
- Localization
- Updates and Maintenance
- Advertisement
Level Of Automation
-
Semi-Autonomous Driving Vehicles
- Level 2
- Level 3
-
Autonomous Driving Vehicles
- Level 4
- Level 5
By Usage Type
- Personal Mobility
- Commercial Mobility
By Service Type
- Passenger Car
- Commercial Vehicles
By Region
-
Asia Oceania
- China
- Japan
- India
- South Korea
-
North America
- US
- Canada
- Mexico
-
Europe
- Germany
- Spain
- Italy
- France
- UK
-
RoW
- Brazil
- South Africa
- Israel
Recent Developments
- In May 2021, HERE and INCREMENT P announced the collaboration for the expansion of the access to high- quality location content and data sets available.
- In February 2021, Dynamic Map Platform and Valeo entered into a partnership to jointly develop technologies and business models for precise localization and map updates to facilitate the development of autonomous driving systems. Both parties have agreed to engage in discussions and technical and business studies of high precision localization and map update technologies necessary for the quality achievement of ADAS and autonomous driving, in the aim of jointly providing services on a global scale and a non-exclusive basis.
- In June 2021, The Sanborn Map Company was awarded a supply contract from Indian Geographic Information Office (GIO) for updating the digital orthoimagery for the entire state of Indiana.
- In January 2021, HERE is providing HD mapping technology to Deutsche Bahn via the HERE location platform. Germany’s railway company will utilize the technology for its new “Sensors4Rail” digital rail project. The aim of this pilot project is to test sensor-based train localization and environment recognition in order to increase railway system capacity, efficiency, and reliability. Other project partners include Bosch, Ibeo, and Siemens Mobility.
- In December 2020, GeoJunxion announced a strategic alliance with NavInfo Europe to offer AI solutions to our customers.
- In March 2020, Momenta has announced a strategic cooperation with Toyota to provide automated HD mapping and updates through vision-based technologies. With this joint development, both companies aim to promote the commercialization of Toyota’s Automated Mapping Platform (AMP) in the China market to better serve Chinese customers.
- In January 2020, Momenta, together with Texas Instruments, unveiled the latest front camera perception product deployed on TI’s latest Jacinto TDA4VM SoC (System on Chip) at the 54th International Consumer Electronics Show (CES 2020), which can help automakers meet the safety requirements for new cars, including Euro NCAP 2022/2024. Momenta is developing a new generation of ADAS solutions based on TI Jacinto TDA4VM SoCs to meet the ever-increasing consumer demands for driving comfort and safety. Momenta’s industry-leading front camera perception and high precision localization algorithms, combined with TI's Jacinto TDA4 processor for ADAS applications, and wide-angle high-resolution cameras, provide effective object detection at long distances and in complex scenarios.
- In September 2019, Silicon Valley startup Civil Maps and the University of Luxembourg’s Interdisciplinary Centre for Security, Reliability, and Trust (SnT) announce a research partnership focused on autonomous driving technology. With this new collaboration, Civil Maps, specialized in providing high-definition maps for autonomous vehicles, has established operations in Luxembourg. The partnership is supported by Luxinnovation and expands the country’s research focus on self-driving vehicles.
Frequently Asked Questions (FAQ):
What is the current size of the HD map for autonomous vehicles market?
The HD map for autonomous vehicles market, by value, is estimated to be USD 1.4 billion in 2021 and is projected to reach USD 16.9 billion by 2030, at a CAGR of 31.7% from 2021 to 2030.
Who are the STARS in the HD map for autonomous vehicles market?
Some of the major players in the HD map for autonomous vehicle market are TomTom (the Netherlands), HERE Technologies (the Netherlands), Waymo (US), NVIDIA (US), Baidu (China), Dynamic Map Platform (Japan), NavInfo (China), and Zenrin (US).
What is the Covid-19 impact on HD maps for autonomous vehicles solution providers?
The COVID-19 pandemic has suspended production, plunged sales, and has compelled key players in the global automotive field to modify or change their strategies. Rescheduling the launch of models and projects, stabilizing dealer networks, managing cash, and reviewing investment portfolios have affected the production and sales of passenger and commercial vehicles across the globe.
What are the new market trends impacting the growth of the HD map for autonomous vehicles market?
HD maps are built and updated in real-time by using the data captured by various sensors, cameras, LiDAR. This data helps autonomous vehicles to make better decisions while performing driving tasks. HD maps can also provide localization information, which will help find the vehicle's accurate position on the map. With recent advancements in computer vision and artificial intelligence, visual Simultaneous Localization and Mapping (SLAM) has gained significant accuracy in determining the position and orientation of a device with respect to its localization while simultaneously mapping the environment around that device. It is expected that HD maps will also provide advertising services, which will be the key revenue-generating segment for HD maps companies.
Which countries are considered in the Asia Oceania region?
Asia Oceania region comprises countries such as China, Japan, India, and South Korea. .
To speak to our analyst for a discussion on the above findings, click Speak to Analyst
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TABLE OF CONTENTS
1 INTRODUCTION (Page No. - 31)
1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION
1.2.1 SEGMENTAL DEFINITIONS, INCLUSIONS, AND EXCLUSIONS
TABLE 1 SEGMENTAL DEFINITIONS, INCLUSIONS, AND EXCLUSIONS: HD MAP FOR AUTONOMOUS VEHICLE MARKET
TABLE 2 OTHER KEY DEFINITIONS
1.3 MARKET SCOPE
1.3.1 MARKETS COVERED
FIGURE 1 MARKETS COVERED: MARKET
1.3.2 YEARS CONSIDERED IN THE STUDY
1.4 LIMITATIONS
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY (Page No. - 36)
2.1 RESEARCH DATA
FIGURE 2 HD MAP FOR AUTONOMOUS VEHICLE MARKET: RESEARCH DESIGN
FIGURE 3 RESEARCH DESIGN MODEL
2.2 SECONDARY DATA
2.2.1 KEY SECONDARY SOURCES FOR BASE NUMBERS (HD MAP FOR AUTONOMOUS VEHICLE)
2.2.2 KEY SECONDARY SOURCES FOR MARKET SIZING
2.2.3 KEY DATA FROM SECONDARY SOURCES
2.3 PRIMARY DATA
FIGURE 4 BREAKDOWN OF PRIMARY INTERVIEWS
2.3.1 PRIMARY PARTICIPANTS
2.4 MARKET SIZE ESTIMATION
2.4.1 TOP-DOWN APPROACH
FIGURE 5 TOP-DOWN APPROACH: MARKET
FIGURE 6 ILLUSTRATION OF HD MAP FOR AUTONOMOUS VEHICLE COMPANY REVENUE ESTIMATION
FIGURE 7 MARKET: DATA TRIANGULATION 1
FIGURE 8 MARKET: DATA TRIANGULATION 2
FIGURE 9 MARKET: RESEARCH DESIGN & METHODOLOGY
2.4.2 DATA TRIANGULATION
2.4.3 FACTORS CONSIDERED FOR THE MARKET FORECAST
TABLE 3 IMPACT OF VARIOUS FACTORS ON THE GROWTH OF HD MAPS FOR AUTONOMOUS VEHICLE MARKET
2.5 MARKET BREAKDOWN
2.6 FACTOR ANALYSIS
2.7 ASSUMPTIONS
2.7.1 KEY ASSUMPTIONS
2.7.2 OTHER RESEARCH ASSUMPTIONS
2.8 RISK ASSESSMENT & RANGES
TABLE 4 RISK ASSESSMENT & RANGES
2.9 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY (Page No. - 51)
FIGURE 10 HD MAP FOR AUTONOMOUS VEHICLE MARKET: MARKET OVERVIEW
FIGURE 11 MARKET, BY REGION, 2021–2030
FIGURE 12 PERSONAL MOBILITY IS ESTIMATED TO LEAD THE HD MAP FOR THE AUTONOMOUS VEHICLE MARKET FROM 2021 TO 2030
4 PREMIUM INSIGHTS (Page No. - 55)
4.1 ATTRACTIVE OPPORTUNITIES IN THE HD MAP FOR AUTONOMOUS VEHICLE MARKET
FIGURE 13 INCREASING DEMAND FOR SEMI-AUTONOMOUS VEHICLES AND RISING INVESTMENT FOR THE DEVELOPMENT OF AUTONOMOUS VEHICLE WILL PROPEL THE HD MAP FOR AUTONOMOUS VEHICLE MARKET
4.2 MARKET, BY REGION
FIGURE 14 ASIA OCEANIA IS PROJECTED TO BE THE FASTEST GROWING REGION IN MARKET DURING 2021−2030
4.3 NORTH AMERICA: MARKET, LEVEL OF AUTOMATION & VEHICLE TYPE
FIGURE 15 NORTH AMERICA: MARKET, BY LEVEL OF AUTOMATION & VEHICLE TYPE, 2021 VS 2030 (USD MILLION)
4.4 MARKET, BY SOLUTION TYPE
FIGURE 16 MARKET, BY SOLUTION TYPE, 2021 VS 2030 (USD MILLION)
4.5 MARKET, BY SERVICE TYPE
FIGURE 17 MARKET, BY SERVICE TYPE, 2021 VS 2030 (USD MILLION)
5 MARKET OVERVIEW (Page No. - 58)
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
FIGURE 18 HD MAPS MARKET: MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Growing trend of autonomous driving
TABLE 5 INVESTMENT BY AUTOMOTIVE COMPANIES IN AUTONOMOUS TECHNOLOGY
5.2.1.2 Increasing adoption of Level 2 and Level 3 ADAS features in the automotive industry
5.2.1.3 Increasing investment by startups in the development of HD maps
TABLE 6 WORLDWIDE DISTRIBUTION OF STARTUPS BY TYPE OF EMBEDDED AUTONOMOUS TECHNOLOGY
5.2.1.4 Increasing partnerships and joint ventures by the key players
TABLE 7 HD MAP MARKET: PARTNERSHIPS/COLLABORATIONS IN HD MAPS ECOSYSTEM
TABLE 8 HD MAP MARKET: OEMS AND ITS HD MAPS SUPPLIERS
TABLE 9 HD MAP MARKET: KEY INVESTMENTS IN HD MAPS ECOSYSTEM
5.2.1.5 Growth in the map data collection
FIGURE 19 CLASSIFICATION OF DIFFERENT ROAD DETECTION APPROACHES DEPENDING ON THE SENSOR AND THE METHODOLOGY
FIGURE 20 DIFFERENT STRATEGIES FOR AUTONOMOUS DRIVING WITH ROAD-LEVEL NAVIGATION AND LANE-LEVEL NAVIGATION
5.2.1.6 Increasing investments in smart city projects
TABLE 10 SMART CITY INITIATIVES AND INVESTMENTS
5.2.2 RESTRAINTS
5.2.2.1 Limited standardization in HD maps
5.2.2.2 Less reliability in untested environments
5.2.3 OPPORTUNITIES
5.2.3.1 Autonomous car renting services
5.2.3.2 Advancement in 5G technology
FIGURE 21 5G TECHNOLOGY ECOSYSTEM
5.2.3.3 Increasing demand for real-time data
FIGURE 22 ARCHITECTURE OF REAL-TIME HD MAP CHANGE DETECTION
FIGURE 23 GENERAL STRUCTURE OF LOCALIZATION PROCEDURE
5.2.4 CHALLENGES
5.2.4.1 Legal & Privacy issues regarding HD maps
5.2.4.2 High cost of the technology and autonomous vehicle mapping
FIGURE 24 HD MAPPING PROCESS
5.2.4.3 Large size data collection, processing, and transmission of HD map data
FIGURE 25 DATA FROM AN AUTONOMOUS VEHICLE
5.3 PATENT ANALYSIS
TABLE 11 PATENTS RELATED TO HD MAP FOR AUTONOMOUS VEHICLE
5.4 PORTER’S FIVE FORCES
FIGURE 26 PORTER’S FIVE FORCES: HD MAP FOR AUTONOMOUS VEHICLE MARKET
5.4.1 THREAT OF NEW ENTRANTS
5.4.1.1 The key players have collaborations with key OEMs
5.4.2 THREAT OF SUBSTITUTES
5.4.3 BARGAINING POWER OF BUYERS
5.4.4 BARGAINING POWER OF SUPPLIERS
5.4.5 RIVALRY AMONG EXISTING COMPETITORS
5.4.5.1 Many HD map providers partnered to develop HD maps and HD mapping datasets
5.4.5.2 Increasing competition to broaden the customer base
5.5 ROADMAP OF HD MAP FOR AUTONOMOUS VEHICLE
FIGURE 27 ROADMAP OF HD MAP FOR AUTONOMOUS VEHICLE IN URBAN ENVIRONMENT
5.6 VALUE CHAIN ANALYSIS
FIGURE 28 VALUE CHAIN ANALYSIS OF HD MAP FOR AUTONOMOUS VEHICLE
5.7 CASE STUDY
5.7.1 INTELLIAS LTD. DEVELOPED FUTURE-PROOF ROAD NETWORK HD MAPS FOR AUTONOMOUS DRIVING EXPERIENCES
5.7.2 DYNAMIC MAP PLATFORM’S CONTRIBUTION TO HONDA LEGEND A LEVEL 3 AUTONOMOUS VEHICLE
5.7.3 DYNAMIC MAP PLATFORM’S CONTRIBUTION TO LEVEL 2 HANDS-OFF DRIVING IN THE NISSAN SKYLINE
5.7.4 DYNAMIC MAP PLATFORM CONTRIBUTION TO AUTONOMOUS DRIVING TEST ON PUBLIC ROADS IN HARIMA SCIENCE GARDEN CITY
5.7.5 PROOF-OF-CONCEPT FOR AUTONOMOUS DRIVING IN THE VICINITY OF IZUKYU SHIMODA STATION AND DEPOPULATED AREAS ALONG THE COAST OF NISHI-IZU, SHIZUOKA PREFECTURE
5.7.6 FUTURISTIC CITY MANAGEMENT WITH 3D CITY MODEL
5.7.7 NEW AGE HD MAPS POWERING AUTONOMOUS VEHICLE
5.7.8 HD MAPS FOR AUTONOMOUS VEHICLE COGNITION
5.7.9 LASER SCANNING RESEARCH GENERATES BENEFITS FOR SOCIETY IN FINLAND
5.7.10 ATLATEC’S APPROACH TO CREATING ACCURATE HD MAPS
5.7.11 PROVIDE <10 CENTI-METER LEVEL LOCALIZATION ACCURACY, USING SMALL LOW-COST SENSORS AND PROCESSORS, ON DISMOUNT, GROUND, AND AERIAL PLATFORMS
5.7.12 MANDLI CAN CREATE DETAILED AND ACCURATE HIGH-DEFINITION DIGITAL MAPS WITHIN A FEW WEEKS AND FOR SPECIFIC GEOGRAPHIC REGIONS.
5.8 ECOSYSTEM ANALYSIS
FIGURE 29 ECOSYSTEM ANALYSIS OF HD MAP FOR AUTONOMOUS VEHICLE
TABLE 12 LIST OF KEY PLAYER FROM HD MAP FOR AUTONOMOUS VEHICLE ECOSYSTEM
FIGURE 30 HD MAP FOR AUTONOMOUS VEHICLE ECOSYSTEM MATRIX
5.9 REGULATORY ANALYSIS OF RELATED MARKETS
5.9.1 GENERAL DATA PROTECTION REGULATION
5.9.2 ADAS: REGULATORY OVERVIEW
5.9.2.1 Canada
5.9.2.2 US
5.9.2.3 European Parliament
5.9.2.4 National agency for automotive safety & victims’ aid (NASVA), Japan
5.9.3 AUTONOMOUS VEHICLE: REGULATORY OVERVIEW
5.9.3.1 Enacted legislation and executive orders in the US
FIGURE 31 ENACTED LEGISLATION AND EXECUTIVE ORDERS IN THE US
5.9.3.2 Autonomous vehicle testing area in China
FIGURE 32 AUTONOMOUS VEHICLE TESTING AREA IN CHINA
5.9.3.3 Autonomous vehicle testing area in Germany
FIGURE 33 AUTONOMOUS VEHICLE TESTING AREA IN GERMANY
5.9.3.4 Autonomous vehicle testing area in Singapore
FIGURE 34 AUTONOMOUS VEHICLE TESTING AREA IN SINGAPORE
5.9.4 REGULATIONS RELATED TO HD MAPS IN CHINA
TABLE 13 CHINA: HD MAP RELATED REGULATIONS
TABLE 14 CHINA AUTONOMOUS VEHICLE REGULATION TIMELINE
5.9.5 KEY REGULATIONS FOR THE DEVELOPMENT OF HD MAPS IN THE US
TABLE 1 KEY REGULATIONS FOR THE DEVELOPMENT OF HD MAPS IN THE US
5.10 PRODUCT ANALYSIS
TABLE 2 PRODUCT ANALYSIS: HD MAP FOR AUTONOMOUS VEHICLE
5.11 HD MAP FOR AUTONOMOUS VEHICLE MARKET: YC-YCC SHIFT
5.12 MARKET, SCENARIOS (2021–2030)
FIGURE 35 MARKET– FUTURE TRENDS & SCENARIOS, 2021–2030 (UNITS)
5.12.1 MOST LIKELY SCENARIO
TABLE 3 MARKET (MOST LIKELY), BY REGION, 2021–2030 (UNITS)
5.12.2 OPTIMISTIC SCENARIO
TABLE 4 MARKET (OPTIMISTIC SCENARIO), BY REGION, 2021–2030 (UNITS)
5.12.3 PESSIMISTIC SCENARIO
TABLE 5 MARKET (PESSIMISTIC), BY REGION, 2021–2030 (UNITS)
6 INDUSTRY TRENDS (Page No. - 103)
6.1 KEY INSIGHTS ON HD MAP DATA FORMATS AND STANDARDS
6.1.1 INSIGHTS ON EXISTING HD MAP FORMATS
6.1.1.1 Autoware Vector Maps
6.1.1.2 OpenDrive
6.1.1.3 Navigation Data Standard (NDS)
FIGURE 36 EXAMPLE LAYERS FROM NDS DATABASE AND SOME SELECTED FEATURES
6.1.1.4 Lanelet2
TABLE 6 COMPARISON OF EXISTING FORMATS OF HD MAPS
TABLE 7 COMPARISON OF HD VECTOR MAP FORMATS BY AUTOWARE MAPS AND FORMATS WORKING GROUP
6.1.2 INSIGHTS ON KEY HD MAP FORMATS
TABLE 8 DATA STORAGE FORMATS FOR HD MAP FOR AUTONOMOUS VEHICLES
TABLE 9 GEOGRAPHIC DATA AND ROAD NETWORK STORAGE FORMATS FOR HD MAP FOR AUTONOMOUS VEHICLES
TABLE 10 TRAFFIC DATA COMMUNICATION FORMATS FOR MAPS FOR AUTONOMOUS VEHICLES
6.1.3 KEY INSIGHTS ON HD MAP ELEMENTS
TABLE 11 KEY INSIGHTS ON HD MAP ELEMENTS
6.1.4 KEY INSIGHTS ON HD MAP ACCURACY
TABLE 12 POTENTIAL STAGES WHERE ERRORS CAN OCCUR IN HD MAP
TABLE 13 MAPPING ERRORS IN HD MAPS
6.1.5 KEY INSIGHTS ON HD MAP DATA COLLECTION METHOD
6.2 INSIGHTS ON KEY INDUSTRY SOLUTIONS FOR LOCALIZATION
TABLE 14 KEY INDUSTRY SOLUTIONS FOR LOCALIZATION
6.3 KEY INSIGHTS ON HD MAP UPDATE & MAINTENANCE
TABLE 15 INSIGHTS ON KEY HD MAP UPDATE STRATEGIES
6.4 KEY SUPPLIERS OF HD MAPS AND THEIR PRODUCT DETAILS
TABLE 16 KEY SUPPLIERS OF HD MAPS AND DETAILS OF HD MAPS PROVIDED BY THEM
6.5 KEY INSIGHTS ON HD MAP DATA OWNERSHIP
6.6 KEY INSIGHTS ON HD MAP DATA COLLECTING METHOD
TABLE 17 DATA COLLECTED BY SURVEY VEHICLES
TABLE 18 LIST OF COMPANIES WHO USES SURVEY-BASED DATA COLLECTION METHODS
TABLE 19 LIST OF COMPANIES WHO USES CROWD SOURCING DATA COLLECTION METHODS
6.7 TECHNOLOGY OVERVIEW
FIGURE 37 FIVE ERAS OF VEHICLE SAFETY
FIGURE 38 CONNECTED VEHICLE FOR AUTONOMOUS DRIVING
6.7.1 HD MAPS PORTFOLIO FOR ALL AUTOMATION LEVELS
TABLE 20 AUTOMATION LEVELS
FIGURE 39 HD MAPS PORTFOLIO FOR ALL AUTOMATION LEVELS
6.7.2 HD MAP LAYERS
FIGURE 40 HD MAP LAYERS
6.7.3 COMPONENTS USED TO BUILD HD MAPS
6.7.3.1 Hardware
FIGURE 41 SENSORS AND THEIR IMPORTANCE IN AUTONOMOUS VEHICLES
6.7.3.1.1 LiDAR
6.7.3.1.2 Camera
6.7.3.1.3 Radar
6.7.3.1.4 Inertial Measurement Unit (IMU)
6.7.3.1.5 Global Positioning System (GPS)
6.7.3.2 Software
FIGURE 42 FLOW CHART FOR PRODUCTION PROCESS AND MAP DATA SOURCES
6.7.4 ADVANCEMENT IN SLAM ALGORITHM
6.7.5 SELF-HEALING MAPS
FIGURE 43 SELF-HEALING MAP PROCESS
6.7.6 MAPPING STANDARDS
FIGURE 44 FEATURES OF KEY MAPPING STANDARDS
6.7.6.1 SENSORIS
6.7.6.2 ADASIS
FIGURE 45 HD MAP STANDARDS
6.7.6.3 Navigation data standard
6.7.6.4 Vector tile 3
6.7.6.5 Open AutoDrive forum
6.7.7 HD 3D VIEWING CUSTOMER EXPERIENCE
6.7.8 EMERGENCE OF AI AND ML TECHNOLOGIES TO BOOST THE 3D CONTENT ACCURACY
6.7.9 ADVENT OF 3D-ENABLED DISPLAY DEVICES FOR A BETTER NAVIGATION EXPERIENCE
6.7.10 3D MAPPING TECHNIQUES
6.7.10.1 Photogrammetry
6.7.10.2 LiDAR
6.7.10.3 RADAR
6.7.10.4 SONAR
FIGURE 46 IOT DEVICES IN AUTONOMOUS VEHICLES
6.7.11 HD MAPPING AND BLOCKCHAIN
6.7.12 MACHINE LEARNING POWERED ANALYTICS
6.7.13 SUPPLY CHAIN 4.0
6.7.14 LIDAR DRONES FOR MAPPING
FIGURE 47 LIDAR ECOSYSTEM: MAJOR VALUE ADDITION BY LIDAR COMPONENT MANUFACTURERS AND THEIR INTEGRATORS & DISTRIBUTORS
6.7.15 LOCATION OF THINGS
6.7.16 HD MAPPING FOR AUTONOMOUS VEHICLE PROVING GROUNDS
6.7.17 POTENTIAL APPLICATIONS OF HD MAPS OTHER THAN AUTONOMOUS VEHICLES
TABLE 21 BUSINESS MODEL FOR AUTONOMOUS LAST MILE DELIVERY
FIGURE 48 ALGORITHMS AND ANALYTICS SUPPORTING AUTONOMOUS LAST MILE DELIVERY
6.7.17.1 Aerial delivery drones
TABLE 22 EVOLUTION OF AERIAL DELIVERY DRONES
6.7.17.1.1 Use cases: Aerial delivery drones
6.7.17.1.2 Market overview: Aerial delivery drones
FIGURE 49 DRIVERS: AERIAL DELIVERY DRONES
FIGURE 50 RESTRAINS: AERIAL DELIVERY DRONES
FIGURE 51 OPPORTUNITIES: AERIAL DELIVERY DRONES
FIGURE 52 CHALLENGES: AERIAL DELIVERY DRONES
6.7.17.2 Ground delivery vehicles
TABLE 23 EVOLUTION OF GROUND DELIVERY VEHICLES
6.7.17.2.1 Use cases: Ground delivery vehicles
6.7.17.2.2 Market overview: Ground delivery vehicles
FIGURE 53 DRIVERS: GROUND DELIVERY VEHICLES
FIGURE 54 RESTRAINS: GROUND DELIVERY VEHICLES
FIGURE 55 OPPORTUNITIES: GROUND DELIVERY VEHICLES
FIGURE 56 CHALLENGES: GROUND DELIVERY VEHICLES
6.7.18 TYPES OF GNSS RECEIVERS
6.7.18.1 Global Positioning System (GPS)
6.7.18.2 Galileo
6.7.18.3 Global Navigation Satellite System (GLONASS)
6.7.18.4 Satellite-based Augmentation System (SBAS)
TABLE 24 SBAS IN DIFFERENT COUNTRIES
6.7.18.5 BeiDou Navigation Satellite System
6.7.19 GIS DATA
6.7.19.1 Types of GIS data models
6.7.19.1.1 Vector data model
6.7.19.1.2 Raster data model
6.7.19.2 Key trends in GIS
6.7.19.2.1 AR and VR technologies
6.7.19.2.2 Unmanned AERIAL Vehicles
6.7.19.2.3 Integration of cloud computing in GIS
6.7.19.2.4 Development of 4D GIS software and augmented reality platforms for GIS
7 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY SERVICE TYPE (Page No. - 145)
7.1 INTRODUCTION
7.2 RESEARCH METHODOLOGY
7.3 ASSUMPTIONS
TABLE 25 ASSUMPTIONS, BY SERVICE TYPE
7.4 OPERATIONAL DATA
FIGURE 57 APPLICATIONS OF HD MAPS FOR AUTONOMOUS VEHICLES
FIGURE 58 HD MAP ECOSYSTEM IN SELF – DRIVING SPACE VEHICLE TECHNOLOGY
FIGURE 59 MARKET, BY SERVICE TYPE, 2021 VS 2030 (USD MILLION)
TABLE 26 MARKET, BY SERVICE TYPE, 2020–2030 (USD MILLION)
7.5 MAPPING
FIGURE 60 MAP INFORMATION REQUIRED FOR SELF-DRIVING VEHICLES
TABLE 27 HD MAP SEMANTIC INFORMATION
TABLE 28 ROAD SEGMENT DATA (RSD) APPLICATIONS
TABLE 29 POTENTIALLY APPLICABLE MAPPING TECHNOLOGIES, THEIR STRENGTHS AND LIMITATIONS
TABLE 30 MAPWORKS SOFTWARE DEVELOPMENT KIT
7.5.1 THE COMPANIES OFFERING END TO END MAPPING SOLUTIONS FOR AUTONOMOUS VEHICLE IS EXPECTED TO DRIVE NORTH AMERICAN MARKET
TABLE 31 MAPPING: HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY REGION, 2020–2030 (USD MILLION)
7.6 LOCALIZATION
FIGURE 61 LOW COST LOCALIZATION WITH CAMERA AND IMU
FIGURE 62 OVERVIEW OF MONOCULAR LOCALIZATION WITH VECTOR MAP
TABLE 32 LOCALIZATION TECHNIQUES FOR AUTONOMOUS VEHICLE AND THEIR ACCURACY
TABLE 33 PRIOR INFORMATION IN HD MAP BASED VEHICLE LOCALIZATION
7.6.1 INCREASED INVESTMENT IN THE LOCALIZATION OF HD MAPS TO DRIVE THE MARKET
TABLE 34 LOCALIZATION: MARKET, BY REGION, 2020–2030 (USD MILLION)
7.7 UPDATES & MAINTENANCE
7.7.1 NEED OF DYNAMIC TRAFFIC, WEATHER INFORMATION AND CONTINUOUSLY UPDATE IN DATA GATHERED BY MAPPING AND LOCALIZATION WILL DRIVE THE MARKET
TABLE 35 UPDATES & MAINTENANCE: MARKET, BY REGION, 2020–2030 (USD MILLION)
7.8 ADVERTISEMENT
7.8.1 ADVERTISEMENT IS PROJECTED TO BE THE FASTEST-GROWING SEGMENT OF HD MAPS FOR THE AUTONOMOUS VEHICLES MARKET
TABLE 36 ADVERTISEMENT: MARKET, BY REGION, 2020–2030 (USD MILLION)
7.9 KEY INDUSTRY INSIGHTS
8 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY LEVEL OF AUTOMATION (Page No. - 158)
8.1 INTRODUCTION
FIGURE 63 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY AUTOMATION LEVEL, 2021 VS. 2030 (USD MILLION)
8.2 RESEARCH METHODOLOGY
8.3 ASSUMPTIONS
TABLE 37 ASSUMPTIONS, BY LEVEL OF AUTOMATION TYPE
8.4 OPERATIONAL DATA
TABLE 38 RECENT AND ONGOING DEMONSTRATION AND TESTING OF CONNECTED AUTONOMOUS VEHICLES BY KEY COMPANIES
TABLE 39 ECONOMIC IMPACT OF CONNECTED AND AUTONOMOUS VEHICLE AND ITS BREAKDOWN
TABLE 40 MARKET, BY AUTOMATION LEVEL, 2020–2030(USD MILLION)
8.5 SEMI-AUTONOMOUS DRIVING VEHICLES
8.5.1 LEVEL 2
8.5.2 LEVEL 3
8.5.3 ASIA OCEANIA REGION IS PROJECTED TO HAVE THE HIGHEST GROWTH RATE
TABLE 41 HD MAP FOR SEMI-AUTONOMOUS VEHICLE MARKET, BY REGION, 2020–2030 (USD MILLION)
TABLE 42 HD MAP FOR SEMI-AUTONOMOUS VEHICLE MARKET, BY LEVEL OF AUTOMATION, 2020–2030 (USD MILLION)
8.6 AUTONOMOUS DRIVING VEHICLES
TABLE 43 EXPECTED TECHNOLOGY VS CURRENT TECHNOLOGY READINESS LEVEL OF AUTONOMOUS VEHICLE
8.6.1 LEVEL 4 & 5 AUTONOMOUS VEHICLE DEVELOPMENT AND DEPLOYMENT
8.6.1.1 Daimler AG
8.6.1.2 TuSimple
FIGURE 64 TUSIMPLE AUTONOMOUS TRUCK ROUTE
FIGURE 65 TUSIMPLE: LEVEL 4 AUTONOMOUS TRUCK BOOKING PORTAL
8.6.1.3 Argo AI and Ford
8.6.1.4 Baidu
8.6.1.5 DiDi
8.6.1.6 Toyota, Pony.ai, and Hyundai
8.6.1.7 Waymo
8.6.1.8 Voyage
8.6.1.9 General Motors and Cruise
8.6.1.10 Volvo
8.6.1.11 Einride
8.6.2 LEVEL 4
8.6.3 LEVEL 5
8.6.4 TECHNOLOGY READINESS OF AUTONOMOUS VEHICLES IN THE EUROPE REGION IS EXPECTED TO DRIVE THE MARKET
TABLE 44 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY REGION, 2020–2030 (USD MILLION)
TABLE 45 MARKET, BY LEVEL OF AUTOMATION, 2020–2030 (USD MILLION)
8.7 KEY INDUSTRY INSIGHTS
9 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY SOLUTION TYPE (Page No. - 173)
9.1 INTRODUCTION
FIGURE 66 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY SOLUTION TYPE, 2021 VS. 2030 (USD MILLION)
9.2 RESEARCH METHODOLOGY
9.3 ASSUMPTIONS
TABLE 46 ASSUMPTIONS: BY SOLUTION TYPE
TABLE 47 MARKET, BY SOLUTION TYPE, 2020–2030 (USD MILLION)
9.4 CLOUD-BASED
FIGURE 67 HD MAP PRODUCTION PIPELINE
FIGURE 68 HD MAP GENERATION IN CLOUD
9.4.1 HIGH FLEXIBILITY AND ACCURACY OFFERED BY CLOUD-BASED HD MAPS ARE EXPECTED TO DRIVE THE MARKET
TABLE 48 CLOUD-BASED: MARKET, BY REGION, 2020–2030 (USD MILLION)
9.5 EMBEDDED
9.5.1 ASIA OCEANIA REGION OFFERS A HUGE OPPORTUNITY FOR THE GROWTH OF EMBEDDED HD MAPS
TABLE 49 EMBEDDED: MARKET, BY REGION, 2020–2030 (USD MILLION)
9.6 KEY INDUSTRY INSIGHTS
10 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY USAGE TYPE (Page No. - 180)
10.1 INTRODUCTION
FIGURE 69 MARKET, BY USAGE TYPE, 2021 VS. 2030 (USD MILLION)
TABLE 50 MARKET, BY USAGE TYPE, 2020–2030 (USD MILLION)
10.2 RESEARCH METHODOLOGY
10.3 ASSUMPTIONS
TABLE 51 ASSUMPTIONS, BY USAGE TYPE
10.4 OPERATIONAL DATA
TABLE 52 FEW POPULAR ROBO-TAXIS USED FOR PASSENGER TRANSPORTATION ACROSS THE WORLD
10.5 PERSONAL MOBILITY
10.5.1 NORTH AMERICA IS ESTIMATED TO BE THE LARGEST MARKET FOR THE PERSONAL MOBILITY SEGMENT
TABLE 53 PERSONAL MOBILITY: HD MAP FOR AUTONOMOUS VEHICLE, BY REGION, 2020–2030 (USD MILLION)
10.6 COMMERCIAL MOBILITY
10.6.1 INCREASING ADOPTION OF RIDE-SHARING SERVICES AND GROWING PARTNERSHIP BETWEEN KEY RIDE-SHARING COMPANIES WITH PROVIDERS OF HD MAPS WILL PROPEL THE MARKET COMMERCIAL MOBILITY SEGMENT
TABLE 54 COMMERCIAL MOBILITY: HD MAP FOR AUTONOMOUS VEHICLE, BY REGION, 2020–2030 (USD MILLION)
10.7 KEY INDUSTRY INSIGHTS
11 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY VEHICLE TYPE (Page No. - 187)
11.1 INTRODUCTION
FIGURE 70 PROCESS OF MAKING A GENERAL HD MAP FOR PASSENGER AND COMMERCIAL VEHICLES
FIGURE 71 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY VEHICLE TYPE, 2021 VS. 2030 (USD MILLION)
11.2 RESEARCH METHODOLOGY
11.3 ASSUMPTIONS
TABLE 55 ASSUMPTIONS: BY VEHICLE TYPE
11.4 OPERATIONAL DATA
TABLE 56 EXISTING PRODUCTS AND SOLUTIONS OFFERED BY HD MAP PROVIDERS
TABLE 57 FEW POPULAR AUTONOMOUS SHUTTLES FROM COMPANIES ACROSS THE WORLD
TABLE 58 TRUCK ROUTING ATTRIBUTES AND CONSIDERATIONS
TABLE 59 US HOURS OF SERVICE REGULATIONS
TABLE 60 KEY PLAYERS IN THE GLOBAL TRUCK PLATOONING MARKET
TABLE 61 MARKET, BY VEHICLE TYPE, 2020–2030 (USD MILLION)
11.5 PASSENGER CAR
11.5.1 THE PASSENGER CAR SEGMENT HOLDS THE LARGEST SHARE OF THE HD MAP FOR THE AUTONOMOUS VEHICLE MARKET
TABLE 62 PASSENGER CAR: MARKET, BY REGION, 2020–2030 (USD MILLION)
11.6 COMMERCIAL VEHICLES
11.6.1 GROWING INVESTMENT BY TRUCK MANUFACTURERS IN AUTONOMOUS VEHICLE TECHNOLOGY TO DRIVE THE COMMERCIAL VEHICLE SEGMENT MARKET
TABLE 63 COMMERCIAL VEHICLE: MARKET, BY REGION, 2020–2030 (USD MILLION)
11.7 KEY INDUSTRY INSIGHTS
12 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY REGION (Page No. - 197)
12.1 INTRODUCTION
TABLE 64 NCAP REGULATIONS: US AND EUROPEAN UNION
TABLE 65 PHASES IN AUTONOMOUS VEHICLE DEVELOPMENT AND IMPACTS
FIGURE 72 HD MAPS FOR AUTONOMOUS MARKET, BY REGION, 2021 VS 2030 (USD MILLION)
TABLE 66 HD MAP FOR AUTONOMOUS VEHICLE MARKET, BY REGION, 2020–2030 (USD MILLION)
12.2 NORTH AMERICA
FIGURE 73 NORTH AMERICA: MARKET SNAPSHOT
TABLE 67 NORTH AMERICA: AUTONOMOUS VEHICLE EFFORTS
TABLE 68 PHASES IN AUTONOMOUS VEHICLE DEVELOPMENT IN THE US AND ITS IMPACT ON HD MAPS
TABLE 69 NORTH AMERICA: MARKET, BY VEHICLE TYPE, 2020–2030 (USD MILLION)
TABLE 70 NORTH AMERICA: MARKET, BY AUTOMATION LEVEL, 2020–2030 (USD MILLION)
TABLE 71 NORTH AMERICA: MARKET, BY USAGE TYPE, 2020–2030 (USD MILLION)
TABLE 72 NORTH AMERICA: MARKET, BY SOLUTION TYPE, 2020–2030 (USD MILLION)
TABLE 73 NORTH AMERICA: MARKET, BY SERVICE TYPE, 2020–2030 (USD MILLION)
12.2.1 US
TABLE 74 US: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.2.2 MEXICO
TABLE 75 MEXICO: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.2.3 CANADA
TABLE 76 CANADA: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.3 ASIA OCEANIA
FIGURE 74 ASIA OCEANIA: SNAPSHOT
TABLE 77 ASIA OCEANIA: AUTONOMOUS VEHICLE EFFORTS
TABLE 78 ASIA OCEANIA: MARKET, BY VEHICLE TYPE, 2020–2030 (USD MILLION)
TABLE 79 ASIA OCEANIA: MARKET, BY AUTOMATION LEVEL, 2020–2030 (USD MILLION)
TABLE 80 ASIA OCEANIA: MARKET, BY USAGE TYPE, 2020–2030 (USD MILLION)
TABLE 81 ASIA OCEANIA: MARKET, BY SOLUTION TYPE, 2020–2030 (USD MILLION)
TABLE 82 ASIA OCEANIA: MARKET, BY SERVICE TYPE, 2020–2030 (USD MILLION)
12.3.1 CHINA
TABLE 83 CHINA: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
TABLE 84 KEY COMPANIES IN AUTONOMOUS VEHICLE TECHNOLOGY AND THEIR KNOWN PARTNERS IN CHINA
TABLE 85 CHINA AUTONOMOUS VEHICLE TESTING RECORDS
12.3.2 JAPAN
TABLE 86 JAPAN: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.3.3 INDIA
TABLE 87 INDIA: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.3.4 SOUTH KOREA
TABLE 88 SOUTH KOREA: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.4 EUROPE
FIGURE 75 EUROPE: HD MAP FOR AUTONOMOUS VEHICLE MARKET SNAPSHOT
TABLE 89 EUROPE: EFFORTS TOWARDS AUTONOMOUS VEHICLES
TABLE 90 EUROPE: MARKET, BY VEHICLE TYPE, 2020–2030 (USD MILLION)
TABLE 91 EUROPE: MARKET, BY LEVEL OF AUTOMATION, 2020–2030 (USD MILLION)
TABLE 92 EUROPE: MARKET, BY USAGE TYPE, 2020–2030 (USD MILLION)
TABLE 93 EUROPE: MARKET, BY SOLUTION TYPE, 2020–2030 (USD MILLION)
TABLE 94 EUROPE: MARKET, BY SERVICE TYPE, 2020–2030 (USD MILLION)
12.4.1 GERMANY
TABLE 95 GERMANY: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.4.2 SPAIN
TABLE 96 SPAIN: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.4.3 ITALY
TABLE 97 ITALY: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.4.4 FRANCE
TABLE 98 FRANCE: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.4.5 UK
TABLE 99 UK: VEHICLE PRODUCTION DATA, 2019-2020 (UNITS)
12.5 ROW
FIGURE 76 ROW: HD MAP FOR AUTONOMOUS VEHICLE MARKET, 2021 VS. 2030 (USD MILLION)
TABLE 100 ROW: MARKET, BY VEHICLE TYPE, 2020–2030 (USD MILLION)
TABLE 101 ROW: MARKET, BY AUTOMATION LEVEL, 2020–2030 (USD MILLION)
TABLE 102 ROW: MARKET, BY USAGE TYPE, 2020–2030 (USD MILLION)
TABLE 103 ROW: MARKET, BY SOLUTION TYPE, 2020–2030 (USD MILLION)
TABLE 104 ROW: MARKET, BY SERVICE TYPE, 2020–2030 (USD MILLION)
12.5.1 BRAZIL
TABLE 105 BRAZIL: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.5.2 SOUTH AFRICA
TABLE 106 SOUTH AFRICA: VEHICLE PRODUCTION DATA, 2018-2020 (UNITS)
12.5.3 ISRAEL
13 COMPETITIVE LANDSCAPE (Page No. - 224)
13.1 OVERVIEW
TABLE 107 OVERVIEW OF STRATEGIES ADOPTED BY KEY PLAYERS IN THE HD MAP FOR AUTONOMOUS VEHICLE MARKET
13.2 MARKET RANKING ANALYSIS
FIGURE 77 MARKET RANKING ANALYSIS: HD MAP FOR AUTONOMOUS VEHICLE MARKET
13.3 COMPETITIVE LEADERSHIP MAPPING
13.3.1 STARS
13.3.2 EMERGING LEADERS
13.3.3 PERVASIVE
13.3.4 PARTICIPANTS
FIGURE 78 HD MAP FOR AUTONOMOUS VEHICLES MARKET: COMPETITIVE LEADERSHIP MAPPING, 2021
TABLE 108 MARKET: COMPANY FOOTPRINT, 2021
TABLE 109 MARKET: SOLUTION FOOTPRINT, 2021
TABLE 110 MARKET: REGIONAL FOOTPRINT, 2021
13.4 SME COMPETITIVE LEADERSHIP MAPPING
13.4.1 PROGRESSIVE COMPANIES
13.4.2 RESPONSIVE COMPANIES
13.4.3 DYNAMIC COMPANIES
13.4.4 STARTING BLOCKS
FIGURE 79 HD MAP FOR AUTONOMOUS VEHICLES MARKET: SME/STARTUP COMPETITIVE LEADERSHIP MAPPING, 2021
TABLE 111 WINNERS VS. TAIL-ENDERS
14 COMPANY PROFILES (Page No. - 234)
(Business Overview, Product Offerings, Recent Developments, Product Launches, Deals, MNM View, Key Strengths/Right to Win, Strategic Choices Made, And Weaknesses and Competitive Treats)*
14.1 NVIDIA
FIGURE 80 FEATURES OF NVIDIA DRIVE
FIGURE 81 DRIVE MAPPING IN THE VEHICLE AND THE CLOUD BY NVIDIA
TABLE 112 NVIDIA: BUSINESS OVERVIEW
FIGURE 82 NVIDIA: COMPANY SNAPSHOT
TABLE 113 NVIDIA: PRODUCT OFFERINGS
TABLE 114 NVIDIA: PRODUCT LAUNCHES
TABLE 115 NVIDIA: DEALS
14.2 TOMTOM
TABLE 116 MAPS OFFERED BY TOMTOM AND ITS FEATURES
TABLE 117 TOMTOM: BUSINESS OVERVIEW
FIGURE 83 TOMTOM: COMPANY SNAPSHOT
TABLE 118 TOMTOM: PRODUCT OFFERINGS
TABLE 119 TOMTOM: PRODUCT LAUNCHES
TABLE 120 TOMTOM: DEALS
14.3 HERE
TABLE 121 HERE: BUSINESS OVERVIEW
FIGURE 84 HERE: COMPANY SNAPSHOT
TABLE 122 HERE: PRODUCT OFFERINGS
TABLE 123 HERE HD: NEW PRODUCT DEVELOPMENTS
TABLE 124 HERE: DEALS
14.4 NAVINFO
TABLE 125 NAVINFO: BUSINESS OVERVIEW
FIGURE 85 NAVINFO: COMPANY SNAPSHOT
TABLE 126 MULTI-LAYER HD MAP BY NAVINFO
TABLE 127 NAVINFO: PRODUCT OFFERINGS
TABLE 128 NAVINFO: PRODUCT LAUNCHES
TABLE 129 NAVINFO: DEALS
14.5 CIVIL MAPS
TABLE 130 CIVIL MAPS: BUSINESS OVERVIEW
TABLE 131 CIVIL MAPS: PRODUCT OFFERINGS
TABLE 132 CIVIL MAPS: PRODUCT LAUNCHES
TABLE 133 CIVIL MAPS: DEALS
14.6 THE SANBORN MAP COMPANY
TABLE 134 THE SANBORN MAP COMPANY: BUSINESS OVERVIEW
TABLE 135 THE SANBORN MAP COMPANY: PRODUCT OFFERINGS
TABLE 136 THE SANBORN MAP COMPANY: PRODUCT LAUNCHES
TABLE 137 THE SANBORN MAP COMPANY: DEALS
14.7 MOMENTA
TABLE 138 MOMENTA: BUSINESS OVERVIEW
TABLE 139 MOMENTA: PRODUCT OFFERINGS
TABLE 140 MOMENTA : PRODUCT LAUNCHES
TABLE 141 MOMENTA: DEALS
14.8 NAVMII
TABLE 142 NAVMII: BUSINESS OVERVIEW
TABLE 143 NAVMII: PRODUCT/SOLUTION OFFERINGS
14.9 DYNAMIC MAP PLATFORM
TABLE 144 DYNAMIC MAP COMPANY: BUSINESS OVERVIEW
TABLE 145 DYNAMIC MAP COMPANY: PRODUCT/SOLUTION OFFERINGS
TABLE 146 DYNAMIC MAP PLATFORM: DEALS
14.10 MAPMYINDIA
TABLE 147 MAPMYINDIA: BUSINESS OVERVIEW
TABLE 148 MAPMYINDIA: PRODUCT/SOLUTION OFFERINGS
TABLE 149 MAPMYINDIA: DEALS
14.11 OTHER KEY PLAYERS
14.11.1 ASIA OCEANIA
14.11.1.1 RMSI
14.11.1.2 Zenrin
14.11.1.3 AutoNavi
14.11.1.4 Baidu
14.11.1.5 Woven Planet
14.11.2 EUROPE
14.11.2.1 Mapillary
14.11.2.2 Blickfeld
14.11.2.3 Geojunxion
14.11.3 NORTH AMERICA
14.11.3.1 CARMERA
14.11.3.2 Voxel Maps
14.11.4 ROW
14.11.4.1 Mobileye
*Details on Business Overview, Product Offerings, Recent Developments, Product Launches, Deals, MNM View, Key Strengths/Right to Win, Strategic Choices Made, And Weaknesses and Competitive Treats not be captured in case of unlisted companies.
15 RECOMMENDATIONS BY MARKETSANDMARKETS (Page No. - 279)
15.1 ASIA PACIFIC EXPECTED TO BE A FASTEST GROWING MARKET
15.2 CLOUD-BASED HD MAP: KEY FOCUS AREA
15.3 GROWING ADOPTION OF HD MAPS FOR COMMERCIAL MOBILITY
15.4 CONCLUSION
16 APPENDIX (Page No. - 281)
16.1 DISCUSSION GUIDE
16.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
16.3 AVAILABLE CUSTOMIZATIONS
16.4 AUTHOR DETAILS
The study involved four major activities in estimating the current size of the HD map for autonomous vehicles market. Exhaustive secondary research was done to collect information on the market, the peer market, and the parent market. The next step was to validate these findings, assumptions, and sizing with the industry experts across value chains through primary research. The top-down and bottom-up approaches were employed to estimate the complete market size. Thereafter, market breakdown and data triangulation processes were used to estimate the market size of segments and subsegments.
Secondary Research
In the secondary research process, various secondary sources such as company annual reports/presentations, press releases, industry association publications [for example[such as country level automotive associations and organizations, Organisation for Economic Co-operation and Development (OECD), World Bank, CDC, and Eurostat]; corporate filings (such as annual reports, investor presentations, and financial statements); and trade, business, and industry associations, magazine articles, directories, technical handbooks, World Economic Outlook, trade websites, government organizations websites, and technical articles have been used to identify and collect information useful for an extensive commercial study of the global HD map for autonomous vehicles market.
Primary Research
Extensive primary research has been conducted after acquiring an understanding of the HD map for autonomous vehicles market scenario through secondary research. Several primary interviews have been conducted with market experts from both, the demand-side (rolling stock manufacturers, railway operators) and supply-side (HD maps for autonomous vehicles solution vendors, software providers, and component manufacturers) across four major regions, namely, North America, Europe, Asia Oceania, and the Rest of the World. Approximately 52% and 48% of primary interviews have been conducted from the demand and supply sides, respectively. Primary data has been collected through questionnaires, emails, and telephonic interviews. In the canvassing of primaries, we have strived to cover various departments within organizations such as sales, operations, and administration, to provide a holistic viewpoint in our report.
After interacting with industry experts, we have also conducted brief sessions with highly experienced independent consultants to reinforce the findings from our primaries. This, along with the in-house subject-matter experts’ opinions, has led us to the findings as described in the remainder of this report.
To know about the assumptions considered for the study, download the pdf brochure
Primary participants
In-depth interviews have been conducted with the target groups to collect industry-related data, technology-related information, and validation of our analysis.
- ADAS providers
- Automotive Components Manufacturers
- Automotive Experts
- Automotive OEMs
- Automotive Service Centers
- Autonomous vehicle manufacturers
- Autonomous vehicle technology developers
- Country-level HD map regulatory authorities
- Country-level Government Associations
- Datasets suppliers
- EV Manufacturers
- Government bodies
- GPS data providers
- Hardware manufacturers
- Industry Associations and Experts
- Mapping companies
- Other Automotive Industry Experts
- Raw Material Suppliers for Hardware Systems
- Sensor manufacturers
- Software providers
- Solutions providers for HD mapping
- Suppliers of HD maps
Market Size Estimation
A detailed market estimation approach was followed to estimate and validate the value of the HD map for autonomous vehicles market and other dependent submarkets, as mentioned below:
- Key players in the HD maps for autonomous vehicles were identified through secondary research, and their global market shares were determined through primary and secondary research.
- The research methodology included the study of the annual and quarterly financial reports & regulatory filings of major market players, as well as interviews with industry experts for detailed market insights.
- All major penetration rates, percentage shares, splits, and breakdowns for the market were determined using secondary sources and verified through primary sources.
- All key macro indicators affecting the revenue growth of the market segments and subsegments were accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the validated and verified quantitative & qualitative data.
- The gathered market data was consolidated and added with detailed inputs, analyzed, and presented in this report
Data Triangulation
All percentage shares, splits, and breakdowns were determined using secondary sources and verified by primary sources. All parameters that are said to affect the markets covered in this research study were accounted for, viewed in extensive detail, and analyzed to obtain the final quantitative and qualitative data. This data was consolidated, enhanced with detailed inputs and analysis from MarketsandMarkets, and presented in the report. The following figure is an illustrative representation of the overall market size estimation process employed for this study.
Report Objectives
- To analyze and forecast the size of the HD map for autonomous vehicles market in terms of value (USD million)
- To analyze and forecast the size of the market based on service type (mapping, localization, updates and maintenance, and advertisement)
- To analyze and forecast the size of the market based on level of automation (semi-autonomous driving vehicles, autonomous driving vehicles)
- To analyze and forecast the size of the market based on solution type (cloud based, embedded)
- To analyze and forecast the size of the market based on usage type (operational data, commercial mobility)
- To analyze and forecast the size of the market based on vehicle type (passenger car, commercial vehicles)
- To analyze and forecast the size of the market based on region (Asia Oceania, North America, Europe, and the Rest of the World (RoW))
- To identify the dynamics, including drivers, restraints, opportunities, and challenges, and analyze their impact on the market
- To track and analyze competitive developments such as new product launches, deals, and others carried out by key industry participants to strengthen their positions in the market
Available Customizations
Along with the market data, MarketsandMarkets offers customizations in line with company-specific needs.
- HD map for autonomous vehicles market, by solution, at the country-level (For countries covered in the report)
-
HD map for autonomous vehicles market, by railcar type, at the country-level (For countries covered in the report)
Company Information
- Profiles of additional market players (Up to five)
Generating Response ...
Growth opportunities and latent adjacency in HD Map for Autonomous Vehicles Market