HD 맵 시장 예측(-2033년) : 자동화 레벨별, 서비스 유형별, 솔루션 유형별, 사용 방법별, 차종별, 지역별

The HD maps market is projected to grow from USD 1.34 billion in 2026 to USD 2.43 billion by 2033, at a CAGR of 8.9%.

The HD maps market is advancing with the rise of semantic mapping, where maps show roads and interpret objects such as traffic lights, crosswalks, and lane markings for contextual awareness. High-definition localization layers are being integrated to support centimeter-level positioning in complex urban areas. Another emerging trend is the use of blockchain and secure data-sharing frameworks to ensure reliability and trust in map updates across fleets. In addition, energy-efficient mapping techniques are gaining attention to reduce the computational load on autonomous vehicles while maintaining accuracy. Data for HD maps is typically collected through fleets of sensor-equipped vehicles using LiDAR, cameras, GPS, and radar, which continuously capture and update detailed road and environment information. This crowdsourced and sensor-fusion approach ensures maps remain precise and up to date for autonomous driving. Improved map accuracy, faster update cycles, precise localization, and lower processing requirements will strengthen the role of HD maps in higher levels of autonomous driving, supporting wider deployment across complex urban roads, highways, and commercial vehicle routes.

Embedded solution to hold a significant market share of the HD maps market during the forecast period.

Market demand for HD Maps is moving toward embedded map architectures that can keep critical road information available inside the vehicle while reducing dependence on continuous cloud connectivity. This is particularly relevant for functions requiring consistent localization and lane-level road context, where network latency or coverage gaps can affect map availability. HD map providers are also developing embedded map content with higher levels of road detail for automated driving functions. For instance, Dynamic Map Platform's Automotive HD Map provides high-precision, detailed road features, including lane geometry, road boundaries, and traffic-related attributes. Additionally, in June 2026, TMAP Mobility announced plans to commercialize its HD map-based 3D lane recognition navigation system in 2027, integrating HD maps with in-vehicle precise positioning and ADAS sensor recognition to provide lane-level guidance across highways and expressways. The market is also moving toward embedded maps that can be refreshed through the vehicle software lifecycle. For instance, Hyundai AutoEver distributes map and navigation updates through OTA, allowing map content stored in the vehicle to be updated without physical service intervention. As embedded mapping becomes more closely connected with vehicle computing, sensor fusion, and OTA infrastructure, competition is shifting toward solutions that can provide low-latency map access while efficiently managing map storage, update frequency, and connectivity requirements within the vehicle. This shift is strengthening demand for embedded HD map architectures that combine local availability with selective OTA updates, enabling OEMs to maintain reliable lane-level context while controlling onboard storage, connectivity dependence, and updating overhead as automated driving functions scale.

Autonomous vehicles are projected to grow at the fastest rate in the HD maps market during the forecast period.

The growth of the autonomous vehicles segment is being driven by the increasing use of HD maps as a core data layer for Level 4 autonomous driving in passenger cars and commercial vehicles. The expansion of L2+/L3 systems and emerging L4 deployment is increasing the need for precise, lane-level road intelligence, particularly for localization, lane topology, and predictive maneuvering. As autonomous vehicles expand into larger operating areas, the ability to create, update, and validate HD maps at scale is becoming as important as map accuracy itself. For instance, in May 2026, Mercedes-Benz announced that its urban point-to-point automated navigation would be available in selected German cities by the end of 2026, with nationwide rollout planned by early 2027, which is also moving automated driving into more complex urban environments. The system is designed for complex urban conditions including traffic lights, lane changes, and dense traffic, increasing the need for detailed road intelligence and precise localization.

OEMs are integrating HD maps directly into automated driving functions. For instance, Mercedes-Benz uses HERE HD Live Map within DRIVE PILOT to support autonomous driving. Similarly, BMW uses HD map information within its Personal Pilot L3 system for highway automated driving. Tier 1 suppliers are integrating HD maps with localization and sensor data. For instance, Bosch is combining vehicle-generated radar and video observations with map data, and ZF is integrating high-precision positioning and connectivity into its automated driving architecture to support higher levels of vehicle automation. For commercial vehicles, HD maps are supporting the expansion of automated driving across defined freight routes, such as DAF Trucks using HERE HD Live Map in the MODI project for Level 4 automated freight operations. As OEMs and Tier 1 suppliers integrate HD maps more deeply into localization, perception, and automated driving architectures, demand is shifting toward high-precision map content, sensor-based updates, cloud connectivity, and scalable geographic coverage, creating opportunities for HD map providers to become technology partners within higher automation vehicle platforms.

Europe is expected to grow at the fastest rate in the HD maps market during the forecast period.

In Europe, the demand is increasingly shifting toward HD map solutions that provide lane-level road geometry, high-precision positioning, road attributes, and frequently refreshed information for advanced passenger vehicle functions. The technical requirement is moving toward maintaining lane-level data across large road networks and integrating this information directly into vehicle software platforms. European passenger car OEMs are increasingly using HD maps as a data layer for automated driving functions. For instance, Volkswagen Group's software company CARIAD is using TomTom Orbis Maps for automated driving systems, while Lotus is integrating high-precision map data with its Highway Navigation Pilot for L2+ driving functions in Europe.

Additionally, HD maps in Europe are also moving toward richer 3D representations that capture road infrastructure and surrounding physical conditions in greater detail. For instance, Dynamic Map Platform provides high-precision 3D point cloud data of motorways and major arterial roads; MINDY Support has also provided enterprise-scale 3D HD map annotation and validation for ADAS and autonomous driving, covering more than 15,000 road infrastructure objects with centimeter-level positional accuracy checks as of February 2026. Across Europe, these market shifts are creating opportunities for HD map providers to combine high-precision lane-level data, frequently updated road information, and 3D road representations into scalable platforms that can support passenger vehicle automation, ADAS development, simulation, validation, and digital road applications.

In-depth interviews were conducted with CEOs, marketing directors, other innovation and technology directors, and executives from various key organizations operating in this market.

• By Company Type: OEM - 18%, Tier I - 74%, and Tier II/III - 8%
• By Designation: CXOs - 36%, Managers - 51%, and Executives - 13%
• By Region: North America - 24%, Europe - 30%, Asia Pacific - 38%, and RoW - 8%

The HD maps market is dominated by major players, including Baidu, Inc. (China), Mobileye (Israel), TomTom International BV (Netherlands), NVIDIA Corporation (US), and HERE (Netherlands). These companies offer highly detailed, lane-level HD maps with real-time updates, designed to support autonomous driving and ADAS. They also provide value-added services such as dynamic map layers (traffic, road conditions, and weather), cloud-based map delivery platforms, crowdsourced data integration, sensor fusion solutions, and software tools for path planning, localization, and navigation.

Research Coverage:

The report covers the HD maps market by Service Type (mapping & localization, update & maintenance, and advertisement), Vehicle Type (PC & CV), Solution Type (embedded & cloud-based), Usage Type, Level of Automation, and Region. It covers the competitive landscape and company profiles of the major HD maps market ecosystem players.

The study also includes an in-depth competitive analysis of key market players, along with company profiles, key observations on product and business offerings, recent developments, and key market strategies.

Key Benefits of Buying the Report:

• The report will help market leaders/new entrants with information on the closest approximations of revenue numbers for the overall HD maps market and its subsegments.
• This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies.
• The report also helps stakeholders understand the market pulse and provides information on key market drivers, restraints, challenges, and opportunities.

The report provides insight on the following pointers:

• Analysis of key drivers (Demand from OEMs for Continuously Refreshed rather than Static HD Maps, Integration of HD Maps into Autonomous Driving and Localization Stacks, and Increasing Adoption of L2 and L3 ADAS-Equipped Vehicles), restraints (High Cost of Creating and Maintaining Lane Level Map Coverage, and Increasing Capability of Onboard Perception to Reduce Dependence on Prebuilt HD Maps), opportunities (Vehicle Generated Data and Maps as a Service Enabling Scalable HD Map Creation and Continuous Updates, Growing Adoption of Dynamic HD Maps and 4D Mapping for Real Time Representation of Changing Road Conditions), and challenges (Maintaining Map Accuracy During Rapid Road Network Changes and Complex Real-time Merging of Multi-Sensor Data).
• Product Development/Innovation: Detailed insights on upcoming technologies and research & development activities in the HD maps market.
• Market Development: Comprehensive information about lucrative markets - the report analyzes the HD maps market across varied regions.
• Market Diversification: Exhaustive information about untapped geographies, recent developments, and investments in the HD maps market.
• Competitive Assessment: In-depth assessment of market share, growth strategies, and product offerings of leading players such as HERE (Netherlands), Baidu, Inc. (China), TomTom International BV (Netherlands), NVIDIA Corporation (US), and Mobileye (Israel) in the HD maps market.

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 STUDY OBJECTIVES
• 1.2 MARKET DEFINITION
• 1.3 STUDY SCOPE
  • 1.3.1 MARKETS COVERED AND REGIONAL SCOPE
  • 1.3.2 INCLUSIONS AND EXCLUSIONS
• 1.4 YEARS CONSIDERED
• 1.5 CURRENCY CONSIDERED
• 1.6 STAKEHOLDERS
• 1.7 SUMMARY OF CHANGES

2 EXECUTIVE SUMMARY

• 2.1 MARKET HIGHLIGHTS AND KEY INSIGHTS
• 2.2 KEY MARKET PARTICIPANTS: MAPPING OF STRATEGIC DEVELOPMENTS
• 2.3 DISRUPTIVE TRENDS IN HD MAPS MARKET
• 2.4 HIGH-GROWTH SEGMENTS
• 2.5 REGIONAL SNAPSHOT: MARKET SIZE, GROWTH RATE, AND FORECAST

3 PREMIUM INSIGHTS

• 3.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN HD MAPS MARKET
• 3.2 HD MAPS MARKET, BY SERVICE TYPE
• 3.3 HD MAPS MARKET, BY VEHICLE TYPE
• 3.4 HD MAPS MARKET, BY SOLUTION TYPE
• 3.5 HD MAPS MARKET, BY LEVEL OF AUTOMATION
• 3.6 HD MAPS MARKET, BY USAGE TYPE
• 3.7 HD MAPS MARKET, BY REGION

4 MARKET OVERVIEW

• 4.1 INTRODUCTION
• 4.2 MARKET DYNAMICS
  • 4.2.1 DRIVERS
    • 4.2.1.1 Shifting preference from static HD maps to updated map data
    • 4.2.1.2 Rising integration of mapping, localization, and ADAS processing into shared software interfaces
    • 4.2.1.3 Increasing adoption of L2 and L3 vehicles
  • 4.2.2 RESTRAINTS
    • 4.2.2.1 High costs of HD map creation and maintenance
    • 4.2.2.2 Increasing capability of onboard perception
  • 4.2.3 OPPORTUNITIES
    • 4.2.3.1 Growing data generation from connected vehicles
    • 4.2.3.2 Increasing reliance on dynamic HD maps and 4D mapping
  • 4.2.4 CHALLENGES
    • 4.2.4.1 Issues in maintaining map accuracy amid rapid road network changes
    • 4.2.4.2 Complexities in achieving seamless multi-sensor fusion
• 4.3 UNMET NEEDS AND WHITE SPACES
  • 4.3.1 LACK OF STANDARDIZED HD MAP FORMATS AND INTERFACES ACROSS AUTONOMOUS DRIVING PLATFORMS
  • 4.3.2 LIMITED COMMERCIAL MODELS FOR CONTINUOUS HD MAP UPDATES AND VEHICLE GENERATED MAP DATA
  • 4.3.3 LACK OF SCALABLE MAP FRESHNESS AND AUTOMATED CHANGE VALIDATION
• 4.4 INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES
• 4.5 STRATEGIC MOVES BY OEMS AND TIER-1/2/3 PLAYERS

5 INDUSTRY TRENDS

• 5.1 MACROECONOMIC OUTLOOK
  • 5.1.1 INTRODUCTION
  • 5.1.2 GDP TRENDS AND FORECAST
  • 5.1.3 TRENDS IN GLOBAL MAPPING INDUSTRY
  • 5.1.4 TRENDS IN GLOBAL AUTONOMOUS VEHICLE INDUSTRY
• 5.2 ECOSYSTEM ANALYSIS
• 5.3 SUPPLY CHAIN ANALYSIS
• 5.4 CASE STUDY ANALYSIS
  • 5.4.1 MINDY SUPPORT DELIVERS ENTERPRISE-SCALE 3D HD MAP ANNOTATION AND VALIDATION FOR ADAS
  • 5.4.2 HERE ENABLES HD MAP-BASED AUTOMATED DRIVING APPLICATIONS FOR GLOBAL OEMS
  • 5.4.3 BAIDU AND TONGJI UNIVERSITY DEVELOP HISTORICAL MAP TRACKING FOR HD MAP CONSTRUCTION
  • 5.4.4 MERCEDES-BENZ INTEGRATES HERE'S HD LIVE MAP INTO DRIVE PILOT SYSTEMS
  • 5.4.5 INTELLIAS CONCEPTUALIZES, DEVELOPS, AND SCALES CLOUD-BASED HD MAPPING AND LOCATION DATA PLATFORM FOR AUTONOMOUS DRIVING
  • 5.4.6 MAGNASOFT DEPLOYS AI-DRIVEN CONTINUOUS IMPROVEMENT PROCESSES THAT ENABLED REAL-TIME DETECTION OF CHANGES IN TRAFFIC PATTERNS AND ROAD CONDITIONS
  • 5.4.7 INFOSYS ADOPTS MULTI-SOURCE HD MAPPING SOLUTION DESIGNED FOR ADAS IN URBAN ENVIRONMENTS
• 5.5 INVESTMENT AND FUNDING SCENARIO
• 5.6 TRADE ANALYSIS
  • 5.6.1 IMPORT SCENARIO (HS CODE 852691)
  • 5.6.2 EXPORT SCENARIO (HS CODE 852691)
• 5.7 KEY CONFERENCES AND EVENTS, 2026-2027
• 5.8 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS

6 TECHNOLOGICAL ADVANCEMENTS, AI-DRIVEN IMPACT, PATENTS, INNOVATIONS, AND FUTURE APPLICATIONS

• 6.1 KEY EMERGING TECHNOLOGIES
  • 6.1.1 AI-BASED HD MAP GENERATION AND AUTOMATED FEATURE EXTRACTION
  • 6.1.2 CROWD-SOURCED AND VEHICLE GENERATED HD MAPPING
  • 6.1.3 DYNAMIC HD MAPS AND CONTINUOUS MAP UPDATING
  • 6.1.4 CLOUD-BASED HD MAP PROCESSING AND DELIVERY
  • 6.1.5 HIGH-PRECISION LOCALIZATION AND MAP MATCHING
• 6.2 COMPLEMENTARY TECHNOLOGIES
  • 6.2.1 SENSOR FUSION
  • 6.2.2 MACHINE LEARNING-POWERED MAP ANALYTICS
• 6.3 ADJACENT TECHNOLOGIES
  • 6.3.1 DIGITAL TWIN AND SIMULATION
  • 6.3.2 VEHICLE EDGE COMPUTING
• 6.4 TECHNOLOGY/PRODUCT ROADMAP
  • 6.4.1 2D VS. 3D VS. 4D MAPPING
  • 6.4.2 SEMANTIC HD MAPS
  • 6.4.3 VECTORIZED HD MAPS
  • 6.4.4 RASTER VS. VECTOR MAP ARCHITECTURE
  • 6.4.5 CLOUD-BASED VS. EMBEDDED HD MAPS
  • 6.4.6 EDGE/ONBOARD MAP PROCESSING
  • 6.4.7 MAP HORIZON
  • 6.4.8 HD MAP APIS AND DATA INTERFACES
  • 6.4.9 ADASIS AND MAP DATA EXCHANGE
• 6.5 PATENT ANALYSIS
• 6.6 IMPACT OF AI/GEN AI ON HD MAPS MARKET
  • 6.6.1 ACCELERATED MAP CREATION AND UPDATES
  • 6.6.2 ENHANCED LOCALIZATION ACCURACY
  • 6.6.3 COST REDUCTION THROUGH SYNTHETIC DATA
  • 6.6.4 DYNAMIC MAP PERSONALIZATION
  • 6.6.5 TOP USE CASES AND MARKET POTENTIAL
  • 6.6.6 BEST PRACTICES FOLLOWED BY MANUFACTURERS/OEMS IN HD MAPS MARKET
  • 6.6.7 CASE STUDIES RELATED TO AI/GEN IMPLEMENTATION IN HD MAPS MARKET
  • 6.6.8 INTERCONNECTED ECOSYSTEM AND IMPACT ON MARKET PLAYERS
  • 6.6.9 CLIENTS' READINESS TO ADOPT AI/GEN AI-INTEGRATED HD MAPS
• 6.7 PHASED ADOPTION STRATEGY OF HD MAPS IN AV ECOSYSTEM
  • 6.7.1 ASSISTED DRIVING AND PILOT DEPLOYMENTS
  • 6.7.2 LIMITED AUTONOMY IN CONTROLLED ENVIRONMENTS
  • 6.7.3 EXPANDED COVERAGE AND REAL-TIME UPDATES
  • 6.7.4 FULL AUTONOMY AND ECOSYSTEM INTEGRATION
• 6.8 HD MAP DATA FORMATS AND STANDARDIZATION LANDSCAPE
  • 6.8.1 CORE COMPONENTS OF HD MAP DATA
    • 6.8.1.1 Geometric layer
    • 6.8.1.2 Semantic layer
    • 6.8.1.3 Localization layer
    • 6.8.1.4 Dynamic/Update layer
  • 6.8.2 HD MAP: KEY STANDARDS AND FORMATS
• 6.9 MNM INSIGHTS ON KEY INDUSTRY SOLUTIONS FOR LOCALIZATION
  • 6.9.1 GNSS AND AUGMENTED POSITIONING
  • 6.9.2 HD MAP-BASED LOCALIZATION
  • 6.9.3 LIDAR AND CAMERA-BASED LOCALIZATION
  • 6.9.4 SENSOR FUSION
  • 6.9.5 V2X-ENABLED COOPERATIVE LOCALIZATION
• 6.10 EVOLVING BUSINESS MODELS FOR HD MAPPING IN AUTONOMOUS MOBILITY
  • 6.10.1 LICENSING AND SUBSCRIPTION-BASED MODEL
  • 6.10.2 CROWDSOURCED MAPPING AND DATA-AS-A-SERVICE (DAAS)
  • 6.10.3 PAY-PER-USE MODEL
• 6.11 HYBRID MAPPING STRATEGIES FOR ENHANCING ADAS DEVELOPMENT
• 6.12 IMPACT OF MAPLESS AUTONOMY CONCEPT ON HD MAPS
• 6.13 ANALYSIS OF OEM HD MAPPING SOLUTIONS
• 6.14 HD MAPPING SERVICE PROVIDERS: COMPETITIVE LANDSCAPE
• 6.15 SUCCESS STORIES AND REAL-WORLD APPLICATIONS
  • 6.15.1 WAYMO: COMMERCIAL ROBOTAXI OPERATIONS IN US
  • 6.15.2 MOBILEYE REM: CROWDSOURCED HD MAPPING ACROSS EUROPE
  • 6.15.3 BAIDU APOLLO GO: CHINA'S ROBOTAXI EXPANSION
  • 6.15.4 DYNAMIC MAP PLATFORM: GLOBAL HD MAP DEPLOYMENT
• 6.16 FUTURE DEPLOYMENT AREAS FOR HD MAPS
  • 6.16.1 AUTONOMOUS DELIVERY VEHICLES
  • 6.16.2 PLATOONING SUPPORT
  • 6.16.3 V2X INTEGRATION
  • 6.16.4 DYNAMIC TRAFFIC ROUTING
  • 6.16.5 FREIGHT CORRIDOR OPTIMIZATION
  • 6.16.6 SMART CITY INTEGRATION
  • 6.16.7 AUTONOMOUS RIDE HAILING AND SHUTTLES
• 6.17 HD MAP MONETIZATION AND REVENUE MODELS
  • 6.17.1 MAP LICENSE
  • 6.17.2 PER-VEHICLE LICENSING
  • 6.17.3 ANNUAL SUBSCRIPTION
  • 6.17.4 PER-KILOMETER PRICING
  • 6.17.5 API-BASED PRICING
  • 6.17.6 CLOUD MAP SUBSCRIPTION
  • 6.17.7 UPDATE/MAINTENANCE FEES
  • 6.17.8 DATA-AS-A-SERVICE
  • 6.17.9 MAP-AS-A-SERVICE
  • 6.17.10 REVENUE SHARING WITH OEMS
  • 6.17.11 BUNDLED HD MAP + LOCALIZATION
  • 6.17.12 BUNDLED MAP + ADAS SOFTWARE
• 6.18 OEM HD MAP DEPENDENCY AND STRATEGY ANALYSIS
  • 6.18.1 OEM FLEET DATA STRATEGY
  • 6.18.2 OEM-OWNED MAPPING ASSETS
  • 6.18.3 OEM-MAP PROVIDER PARTNERSHIPS
  • 6.18.4 OEM-CLOUD PROVIDER PARTNERSHIPS
  • 6.18.5 OEM-AV SOFTWARE INTEGRATION
• 6.19 OEM MAP LOCALIZATION STRATEGY, BY REGION
  • 6.19.1 ASIA PACIFIC
  • 6.19.2 EUROPE
  • 6.19.3 NORTH AMERICA
• 6.20 REGIONAL OEM STRATEGIES FOR HD MAP ADOPTION
• 6.21 HD MAP PROVIDER STRATEGIC CAPABILITY BENCHMARK
  • 6.21.1 GLOBAL ROAD COVERAGE
  • 6.21.2 MAP ACCURACY
  • 6.21.3 MAP UPDATE CAPABILITY
  • 6.21.4 CROWDSOURCED DATA SCALE
  • 6.21.5 OEM INTEGRATION
  • 6.21.6 AUTONOMOUS DRIVING CAPABILITY
  • 6.21.7 COMMERCIAL MODEL
• 6.22 EVOLUTION OF HD MAPS FOR AUTONOMOUS DRIVING
  • 6.22.1 EVOLUTION FROM SD MAPS TO DYNAMIC HD MAPS
  • 6.22.2 EVOLUTION FROM STATIC MAP TO CONTINUOUSLY UPDATED MAP
• 6.23 HD MAPS: TOTAL COST OF OWNERSHIP ANALYSIS
  • 6.23.1 OEM TOTAL COST OF OWNERSHIP PER VEHICLE
  • 6.23.2 HD MAP SUPPLIER TOTAL COST OF OWNERSHIP PER KILOMETER

7 REGULATORY LANDSCAPE AND SUSTAINABILITY INITIATIVES

• 7.1 REGULATORY LANDSCAPE
  • 7.1.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
    • 7.1.1.1 North America
    • 7.1.1.2 Europe
    • 7.1.1.3 Asia Pacific
    • 7.1.1.4 RoW
  • 7.1.2 KEY REGULATIONS
    • 7.1.2.1 US
    • 7.1.2.2 Germany
    • 7.1.2.3 China
    • 7.1.2.4 Japan
    • 7.1.2.5 South Korea
    • 7.1.2.6 UK
  • 7.1.3 REGULATIONS AND INITIATIVES FOR ADAS
    • 7.1.3.1 US
    • 7.1.3.2 China
    • 7.1.3.3 Japan
    • 7.1.3.4 European Union
    • 7.1.3.5 UK
    • 7.1.3.6 Germany
    • 7.1.3.7 France
    • 7.1.3.8 India
  • 7.1.4 NCAP REGULATIONS
    • 7.1.4.1 US
    • 7.1.4.2 Europe
    • 7.1.4.3 Australia & New Zealand
    • 7.1.4.4 South Korea
    • 7.1.4.5 China
    • 7.1.4.6 Latin America
    • 7.1.4.7 Southeast Asia
    • 7.1.4.8 India
• 7.2 SUSTAINABILITY INITIATIVES
  • 7.2.1 CHINA: INTELLIGENT CONNECTED VEHICLE AND DIGITAL ROAD INFRASTRUCTURE INITIATIVES
  • 7.2.2 EUROPE: DIGITAL AND SUSTAINABLE TRANSPORT INFRASTRUCTURE INITIATIVES
  • 7.2.3 US: SMART TRANSPORTATION AND CONNECTED VEHICLE INITIATIVES
  • 7.2.4 JAPAN: SMART ROAD AND CONNECTED MOBILITY INITIATIVES
  • 7.2.5 SOUTH KOREA: INTELLIGENT TRANSPORTATION AND AUTONOMOUS MOBILITY INITIATIVES
  • 7.2.6 UAE: SMART MOBILITY AND DIGITAL ROAD INFRASTRUCTURE INITIATIVES
  • 7.2.7 INDIA: INTELLIGENT TRANSPORTATION AND DIGITAL ROAD INFRASTRUCTURE INITIATIVES
  • 7.2.8 GERMANY: SMART ROAD AND AUTOMATED DRIVING INFRASTRUCTURE INITIATIVES
• 7.3 IMPACT OF REGULATORY POLICIES ON SUSTAINABILITY INITIATIVES

8 CUSTOMER LANDSCAPE AND BUYER BEHAVIOR

• 8.1 DECISION-MAKING PROCESS
• 8.2 KEY STAKEHOLDERS INVOLVED IN BUYING PROCESS AND THEIR EVALUATION CRITERIA
  • 8.2.1 KEY STAKEHOLDERS IN BUYING PROCESS
  • 8.2.2 BUYING CRITERIA
• 8.3 ADOPTION BARRIERS AND INTERNAL CHALLENGES
  • 8.3.1 MARKET PROFITABILITY
  • 8.3.2 REVENUE POTENTIAL
  • 8.3.3 COST DYNAMICS
  • 8.3.4 MARGIN OPPORTUNITIES, BY APPLICATION

9 HD MAPS MARKET, BY LEVEL OF AUTOMATION

• 9.1 INTRODUCTION
• 9.2 SEMI-AUTONOMOUS VEHICLE
  • 9.2.1 LEVEL 2/2+
    • 9.2.1.1 Strong focus on safety, convenience, and ADAS regulation compliance to foster segmental growth
  • 9.2.2 LEVEL 3
    • 9.2.2.1 High emphasis on precise localization and safe driving during traffic congestion to boost segmental growth
• 9.3 AUTONOMOUS VEHICLE
  • 9.3.1 LEVEL 4
    • 9.3.1.1 Ability to tackle complex driving situations and operate under pretrained or predefined conditions to fuel segmental growth
  • 9.3.2 LEVEL 5
    • 9.3.2.1 Focus on enabling fully driverless operation to contribute to segmental growth
• 9.4 KEY PRIMARY INSIGHTS

10 HD MAPS MARKET, BY SERVICE TYPE

• 10.1 INTRODUCTION
• 10.2 MAPPING & LOCALIZATION
  • 10.2.1 RAPID ADVANCES IN AI, MACHINE LEARNING, AND SENSOR TECHNOLOGIES TO EXPEDITE SEGMENTAL GROWTH
• 10.3 UPDATE & MAINTENANCE
  • 10.3.1 GROWING EMPHASIS ON MAINTAINING PRECISION IN AUTONOMOUS DRIVING AND ADJUSTING TO CHANGING ENVIRONMENTS TO DRIVE MARKET
• 10.4 ADVERTISEMENT
• 10.5 KEY PRIMARY INSIGHTS

11 HD MAPS MARKET, BY SOLUTION TYPE

• 11.1 INTRODUCTION
• 11.2 EMBEDDED
  • 11.2.1 USE TO OFFER REAL-TIME, HIGH-PRECISION LOCALIZATION AND NAVIGATION TO AUGMENT SEGMENTAL GROWTH
• 11.3 CLOUD-BASED
  • 11.3.1 ABILITY TO MAKE CONTINUOUS UPDATES AND PROCESS VAST AMOUNTS OF DATA FROM SENSORS AND CAMERAS TO SPUR DEMAND
• 11.4 KEY PRIMARY INSIGHTS

12 HD MAPS MARKET, BY USAGE TYPE

• 12.1 INTRODUCTION
• 12.2 PERSONAL MOBILITY
  • 12.2.1 GROWING DEMAND FOR ADAS-EQUIPPED PASSENGER CARS TO ACCELERATE SEGMENTAL GROWTH
• 12.3 COMMERCIAL MOBILITY
  • 12.3.1 RISING POPULARITY OF E-COMMERCE AND DEMAND FOR EFFICIENT DELIVERY AND TRANSPORT TO FUEL SEGMENTAL GROWTH
• 12.4 KEY PRIMARY INSIGHTS

13 HD MAPS MARKET, BY VEHICLE TYPE

• 13.1 INTRODUCTION
• 13.2 PASSENGER CAR
  • 13.2.1 GROWING INTEGRATION OF ADAS AND SEMI-AUTONOMOUS FEATURES INTO MID- TO HIGH-END VEHICLES TO FACILITATE SEGMENTAL GROWTH
• 13.3 COMMERCIAL VEHICLE
  • 13.3.1 HIGH EMPHASIS ON SUPPORTING ROUTE OPTIMIZATION, PREDICTIVE MAINTENANCE, AND LIVE TRAFFIC MONITORING TO DRIVE MARKET
• 13.4 KEY PRIMARY INSIGHTS

14 HD MAPS MARKET, BY REGION

• 14.1 INTRODUCTION
• 14.2 ASIA PACIFIC
  • 14.2.1 INCREASING INVESTMENT IN AUTONOMOUS DRIVING AND ADVANCED MAPPING TECHNOLOGIES TO DRIVE MARKET
  • 14.2.2 CHINA
  • 14.2.3 INDIA
  • 14.2.4 JAPAN
  • 14.2.5 SOUTH KOREA
• 14.3 EUROPE
  • 14.3.1 RISING REGULATORY FOCUS ON VEHICLE SAFETY AND AUTONOMOUS DRIVING TO FOSTER MARKET GROWTH
  • 14.3.2 GERMANY
  • 14.3.3 FRANCE
  • 14.3.4 ITALY
  • 14.3.5 SPAIN
  • 14.3.6 UK
• 14.4 NORTH AMERICA
  • 14.4.1 MOUNTING ADOPTION OF CONNECTED AND INTELLIGENT VEHICLE TECHNOLOGIES TO BOOST MARKET GROWTH
  • 14.4.2 US
  • 14.4.3 CANADA
  • 14.4.4 MEXICO
• 14.5 ROW
  • 14.5.1 INCREASING SMART MOBILITY INVESTMENT AND TECHNOLOGY PARTNERSHIPS TO AUGMENT MARKET GROWTH
  • 14.5.2 BRAZIL
  • 14.5.3 SOUTH AFRICA
  • 14.5.4 RUSSIA
  • 14.5.5 SAUDI ARABIA
  • 14.5.6 UAE

15 COMPETITIVE LANDSCAPE

• 15.1 OVERVIEW
• 15.2 KEY PLAYER COMPETITIVE STRATEGIES/RIGHT TO WIN, 2024-2026
• 15.3 MARKET SHARE ANALYSIS, 2025
• 15.4 REVENUE ANALYSIS, 2021-2025
• 15.5 COMPANY VALUATION AND FINANCIAL METRICS
• 15.6 BRAND/PRODUCT COMPARISON
  • 15.6.1 HERE (NETHERLANDS)
  • 15.6.2 BAIDU (CHINA)
  • 15.6.3 TOMTOM INTERNATIONAL BV (NETHERLANDS)
  • 15.6.4 NVIDIA CORPORATION (US)
  • 15.6.5 MOBILEYE (ISRAEL)
• 15.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2025
  • 15.7.1 STARS
  • 15.7.2 EMERGING LEADERS
  • 15.7.3 PERVASIVE PLAYERS
  • 15.7.4 PARTICIPANTS
  • 15.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2025
    • 15.7.5.1 Company footprint
    • 15.7.5.2 Region footprint
    • 15.7.5.3 Usage type footprint
    • 15.7.5.4 Vehicle type footprint
    • 15.7.5.5 Level of automation footprint
• 15.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2025
  • 15.8.1 PROGRESSIVE COMPANIES
  • 15.8.2 RESPONSIVE COMPANIES
  • 15.8.3 DYNAMIC COMPANIES
  • 15.8.4 STARTING BLOCKS
  • 15.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2025
    • 15.8.5.1 Detailed list of key startups/SMEs
    • 15.8.5.2 Competitive benchmarking of key startups/SMEs
• 15.9 COMPETITIVE SCENARIO
  • 15.9.1 PRODUCT/SERVICE LAUNCHES/DEVELOPMENTS
  • 15.9.2 DEALS
  • 15.9.3 EXPANSIONS
  • 15.9.4 OTHER DEVELOPMENTS

16 COMPANY PROFILES

• 16.1 KEY PLAYERS
  • 16.1.1 HERE
    • 16.1.1.1 Business overview
    • 16.1.1.2 Products/Solutions/Services offered
    • 16.1.1.3 Recent developments
      • 16.1.1.3.1 Deals
      • 16.1.1.3.2 Other developments
    • 16.1.1.4 MnM view
      • 16.1.1.4.1 Key strengths/Right to win
      • 16.1.1.4.2 Strategic choices
      • 16.1.1.4.3 Weaknesses/Competitive threats
  • 16.1.2 BAIDU
    • 16.1.2.1 Business overview
    • 16.1.2.2 Products/Solutions/Services offered
    • 16.1.2.3 Recent developments
      • 16.1.2.3.1 Deals
    • 16.1.2.4 MnM view
      • 16.1.2.4.1 Key strengths/Right to win
      • 16.1.2.4.2 Strategic choices
      • 16.1.2.4.3 Weaknesses/Competitive threats
  • 16.1.3 TOMTOM INTERNATIONAL BV
    • 16.1.3.1 Business overview
    • 16.1.3.2 Products/Solutions/Services offered
    • 16.1.3.3 Recent developments
      • 16.1.3.3.1 Product/Service launches/enhancements
      • 16.1.3.3.2 Deals
      • 16.1.3.3.3 Other developments
    • 16.1.3.4 MnM view
      • 16.1.3.4.1 Key strengths/Right to win
      • 16.1.3.4.2 Strategic choices
      • 16.1.3.4.3 Weaknesses/Competitive threats
  • 16.1.4 NVIDIA CORPORATION
    • 16.1.4.1 Business overview
    • 16.1.4.2 Products/Solutions/Services offered
    • 16.1.4.3 Recent developments
      • 16.1.4.3.1 Deals
    • 16.1.4.4 MnM view
      • 16.1.4.4.1 Key strengths/Right to win
      • 16.1.4.4.2 Strategic choices
      • 16.1.4.4.3 Weaknesses/Competitive threats
  • 16.1.5 MOBILEYE
    • 16.1.5.1 Business overview
    • 16.1.5.2 Products/Solutions/Services offered
    • 16.1.5.3 Recent developments
      • 16.1.5.3.1 Deals
      • 16.1.5.3.2 Other developments
    • 16.1.5.4 MnM view
      • 16.1.5.4.1 Key strengths/Right to win
      • 16.1.5.4.2 Strategic choices
      • 16.1.5.4.3 Weaknesses/Competitive threats
  • 16.1.6 WAYMO LLC
    • 16.1.6.1 Business overview
    • 16.1.6.2 Products/Solutions/Services offered
    • 16.1.6.3 Recent developments
      • 16.1.6.3.1 Deals
      • 16.1.6.3.2 Expansions
      • 16.1.6.3.3 Other developments
  • 16.1.7 DYNAMIC MAP PLATFORM CO., LTD.
    • 16.1.7.1 Business overview
    • 16.1.7.2 Products/Solutions/Services offered
    • 16.1.7.3 Recent developments
      • 16.1.7.3.1 Product/Service launches/enhancements
      • 16.1.7.3.2 Deals
      • 16.1.7.3.3 Expansions
      • 16.1.7.3.4 Other developments
  • 16.1.8 NAVINFO CO., LTD.
    • 16.1.8.1 Business overview
    • 16.1.8.2 Products/Solutions/Services offered
    • 16.1.8.3 Recent developments
      • 16.1.8.3.1 Deals
      • 16.1.8.3.2 Other developments
  • 16.1.9 THE SANBORN MAP COMPANY, INC.
    • 16.1.9.1 Business overview
    • 16.1.9.2 Products/Solutions/Services offered
    • 16.1.9.3 Recent developments
      • 16.1.9.3.1 Developments
  • 16.1.10 MOMENTA
    • 16.1.10.1 Business overview
    • 16.1.10.2 Products/Solutions/Services offered
  • 16.1.11 MAPBOX
    • 16.1.11.1 Business overview
    • 16.1.11.2 Products/Solutions/Services offered
    • 16.1.11.3 Recent developments
      • 16.1.11.3.1 Product/Service launches/enhancements
      • 16.1.11.3.2 Deals
  • 16.1.12 CE INFO SYSTEMS LTD.
    • 16.1.12.1 Business overview
    • 16.1.12.2 Products/Solutions/Services offered
    • 16.1.12.3 Recent developments
      • 16.1.12.3.1 Deals
• 16.2 OTHER PLAYERS
  • 16.2.1 NAVMII
  • 16.2.2 RMSI
  • 16.2.3 ZENRIN CO., LTD.
  • 16.2.4 WOVEN BY TOYOTA, INC.
  • 16.2.5 SWIFT NAVIGATION, INC.
  • 16.2.6 EXLSERVICE HOLDINGS, INC.
  • 16.2.7 ROBOTIC DATA
  • 16.2.8 HYUNDAI AUTOEVER CORP.
  • 16.2.9 GENESYS INTERNATIONAL CORPORATION LTD
  • 16.2.10 GEOMATE
  • 16.2.11 INTELLIAS
  • 16.2.12 MORAI INC.
  • 16.2.13 TRIMBLE INC.

17 RESEARCH METHODOLOGY

• 17.1 RESEARCH DATA
  • 17.1.1 SECONDARY DATA
    • 17.1.1.1 List of key secondary sources
    • 17.1.1.2 Key data from secondary sources
  • 17.1.2 PRIMARY DATA
    • 17.1.2.1 Key data from primary sources
    • 17.1.2.2 Key industry insights
    • 17.1.2.3 List of primary interview participants
    • 17.1.2.4 Breakdown of primary interviews
• 17.2 MARKET SIZE ESTIMATION
  • 17.2.1 TOP-DOWN APPROACH
  • 17.2.2 MARKET FORECAST APPROACH
    • 17.2.2.1 Supply side
    • 17.2.2.2 Demand side
• 17.3 DATA TRIANGULATION
• 17.4 FACTOR ANALYSIS
• 17.5 RESEARCH ASSUMPTIONS
• 17.6 RESEARCH LIMITATIONS
• 17.7 RISK ANALYSIS

18 APPENDIX

• 18.1 DISCUSSION GUIDE
• 18.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
• 18.3 CUSTOMIZATION OPTIONS
  • 18.3.1 HD MAPS MARKET, BY SEMI AUTONOMOUS VEHICLES, AT REGIONAL LEVEL
  • 18.3.2 HD MAPS MARKET, BY AUTONOMOUS VEHICLES, AT REGIONAL LEVEL
  • 18.3.3 HD MAPS MARKET, BY VEHICLE TYPE, BY LEVEL OF AUTOMATION
  • 18.3.4 COMPANY INFORMATION
    • 18.3.4.1 Profiling of additional market players (up to five)
• 18.4 RELATED REPORTS
• 18.5 AUTHOR DETAILS