Next-Generation Automotive Computing Market 2026-2036: ADAS, AI In-Cabin Monitoring, Centralization, and Connected Vehicles

1.1 Market Overview
1.2 Key Technology Trends
1.2.1 Centralization Dominates Architecture Evolution
1.2.2 Chinese Ecosystem Disruption
1.2.3 L2 Emerges as Critical Middle Ground
1.2.4 In-Cabin Sensing Regulatory Wave
1.2.5 Software Defining Value
1.2.6 Chiplet Technology Promises Flexibility
1.3 Regional Market Dynamics

2.1 Connected Vehicles
2.2 Localization
2.3 AI and Training
2.4 Teleoperation
2.5 Cybersecurity
2.6 Autonomous Vehicle Sensors
2.6.1 Autonomous Driving Technologies
2.6.2 The Primary Three Sensors - Cameras, Radar, and LiDAR
2.6.3 Sensor Performance and Trends
2.6.3.1 Radar Evolution
2.6.3.2 LiDAR Evolution
2.6.4 Robustness to Adverse Weather
2.6.5 Evolution of Sensor Suite From Level 1 to Level 4
2.6.6 What is Sensor Fusion?
2.6.6.1 Fusion Architectures
2.6.6.2 Fusion Challenges and Research Frontiers
2.7 Autonomy and Electric Vehicles
2.7.1 EV Range Reduction
2.7.2 The Vulnerable Road User Challenge in City Traffic
2.7.3 Pedestrian Risk Detection
2.7.3.1 Risk Assessment Factors
2.7.3.2 Multi-Modal Risk Fusion
2.7.4 Recommended Sensor Suites For SAE Level 2 to Level 4 & Robotaxi
2.7.4.1 Key Evolutionary Trends
2.8 Cameras
2.8.1 Technical Specifications
2.8.2 Placement Optimization
2.8.3 AI Processing Pipeline
2.8.4 Limitations and Failure Modes
2.8.5 IR Cameras
2.8.5.1 Short-Wave Infrared (SWIR)
2.9 Radar
2.9.1 Technical Specifications
2.9.2 Advantages Over LiDAR
2.9.3 Limitations
2.9.4 Future Trajectory
2.10 LiDAR
2.10.1 LiDAR Fundamentals
2.10.2 LiDAR Scanning Mechanisms
2.10.2.1 Mechanical Spinning Systems
2.10.2.2 MEMS Mirror Scanning
2.10.2.3 Solid-State Flash LiDAR
2.10.2.4 Frequency-Modulated Continuous Wave (FMCW)
2.10.3 Automotive LiDAR Performance
2.10.4 Key Advantages
2.10.5 Limitations
2.10.6 Future Outlook

3.1 SAE Levels of Driving Automation (L0-L5)
3.1.1 Key Distinctions Between Levels
3.1.2 Level 2, Level 2 , and Level 3 Definitions
3.2 Summary of Privately Owned Autonomous Vehicles
3.2.1 Level 0 - No Automation
3.2.2 Level 2 - Enhanced Partial Automation
3.2.3 Level 2 (Partial Automation)
3.2.4 Level 2 (Enhanced Partial Automation)
3.2.4.1 Chinese L2 Market Leadership
3.2.4.2 L2 Emergence as De Facto Category
3.2.4.3 L2 Regulatory Evolution
3.2.4.4 L2 Market Penetration Forecast
3.2.4.5 Level 2 Could Be Long-Term Middle Ground
3.2.4.6 L2 Technology Improving Rapidly (Closing Gap with L3):
3.2.4.7 Tesla's L2 Strategy Validating Approach:
3.2.4.8 Economic Pressure Favoring L2
3.2.5 Level 3 - Conditional Automation
3.2.5.1 Current ODD Limitations (2024-2025)
3.2.5.2 Why L3 Deployment is Limited (2024-2025)
3.2.5.3 Biggest Barriers to L3 or L4 - Liability
3.2.6 Level 4 - High Automation
3.2.7 Level 5 - Full Automation
3.3 Roadmap of Autonomous Driving Functions in Private Cars
3.3.1 Historical Evolution (2000-2024)
3.3.2 Current State (2024-2025)
3.3.3 Roadmap by Region (2024-2036)
3.3.3.1 North America
3.3.3.2 Europe
3.3.3.3 China
3.3.3.4 Japan
3.4 L2 and L2 Autonomous Driving Systems and Brands
3.4.1 System Technology
3.4.1.1 Chinese L2 Systems
3.5 ADAS Features
3.5.1 AEB (Automatic Emergency Braking)
3.5.2 Luxury ADAS Features: CC/ACC (Cruise Control / Adaptive Cruise Control)
3.5.3 LDW/LKA/LCA (Lane Departure Warning / Lane Keep Assist / Lane Change Assist)
3.5.4 BSM/BSD (Blind Spot Monitoring/Detection)
3.5.5 Signal Recognition (TSR - Traffic Sign Recognition)
3.5.6 Rear/360° Parking (Cameras)
3.5.7 Auto Parking (Automated Parking Assist)
3.6 Overview of ADAS Market Trends
3.6.1 Major Developments 2023-202
3.6.2 Year-on-Year Increase in SAE Level 2 Adoption
3.6.3 China's Dominance
3.6.4 Europe's Regulatory-Driven Growth
3.6.5 US Market Dynamics
3.6.6 High Levels of Autonomy Means More Sensors per Vehicle:
3.6.7 LiDAR is for Level 3 and the Chinese Market:
3.6.7.1 LiDAR Market Forecast Implications
3.7 L2 /L3 Feature Adoption Forecast by Region
3.7.1 Global L2 /L3 Feature Adoption Forecast
3.7.1.1 United States
3.7.1.2 China
3.7.1.3 Europe
3.7.1.4 Japan
3.8 Global Vehicle Sales and Peak Car by SAE Level: 2022-2045
3.9 SAE Level Evolution
3.9.1 L0/L1 (No/Minimal ADAS) - Regulatory Extinction
3.9.2 L2 (Combined ACC LKA) - Peak and Plateau
3.9.3 L2 (Hands-Off, Eyes-On) - Rapid Growth to Mainstream
3.9.4 L3 (Conditional Automation) - Premium Niche to Mainstream
3.9.5 L4 (High/Full Automation) - Emerging Personal Vehicles
3.9.6 Peak Car Analysis - Developed vs. Emerging Markets
3.9.7 Implications for ADAS Market
3.10 Comparison of Multi-Sensor and Pure Vision Solutions
3.11 End-to-End (E2E) Architecture
3.11.1 Traditional Modular Pipeline vs. End-to-End Architecture
3.11.2 Advantages of E2E
3.11.3 Challenges of E2E
3.11.4 Deployment of End-to-End Models in Vehicles
3.11.5 Why Most OEMs Not Adopting E2E
3.12 Sensor suite for ADAS cars
3.12.1 Evolution of Sensor Suite From Level 1 to Level 4
3.12.2 Cost Implications
3.12.3 Sensors and Their Purpose
3.12.4 Sensor Complementarity (Why Multi-Sensor Fusion)
3.12.5 Evolution of Sensor Suites from Level 1 to Level 4
3.12.6 Sensor Count Trends
3.12.7 Camera Systems
3.12.8 Typical Sensor Suite for ADAS Passenger Cars - Camera and Radar
3.12.8.1 Integrated Front-View Cameras
3.12.8.2 Regulatory Drivers for Camera ADAS
3.12.8.3 Performance Trends
3.12.8.4 External Cameras for Autonomous Driving
3.12.9 Radar Systems
3.12.9.1 Front Radar Applications
3.12.9.2 The Role of Side Radars
3.12.9.3 Front and Side Radars per Car
3.12.9.4 Total Radars per Car for Different SAE Levels
3.12.9.5 4D Imaging Radar - Next Generation
3.12.10 LiDAR Systems
3.12.10.1 LiDAR Deployment
3.12.10.2 Automotive LiDAR Players by Technology
3.12.10.3 LiDAR Cost Trajectory and Mass-Market Viability
3.13 Market Challenges and Evolution
3.13.1 China's Top 4 LiDAR Manufacturers Dominate 2024 Market
3.13.1.1 Why Chinese LiDAR Dominance?
3.13.2 ADAS Tier 1 Suppliers Facing Unprecedented Challenges
3.13.2.1 Tier-1 Strategic Responses
3.13.2.2 Market Outlook - Tier-1 Consolidation
3.14 Autonomous Vehicle Adoption and Revenue Forecasts by Region
3.14.1 United States: 2022-2045
3.14.2 China: 2022-2044
3.14.3 Europe (EU UK EFTA): 2022-2044
3.14.4 Japan: 2022-2044
3.15 Regional Dynamics
3.15.1 China's Dominance Accelerating
3.15.2 US Market - Profitable but Slower Growth
3.15.3 Europe - Regulatory Leadership, Technology Lag
3.15.4 Japan - Falling Behind
3.15.5 Rest of World - Emerging Opportunity
3.16 Passenger ADAS Vehicle Market Readiness
3.16.1 ADAS Feature Deployment in US
3.16.2 ADAS Feature Deployment in China
3.16.2.1 China ADAS Ecosystem
3.16.2.2 China L2 / NOA Solution Providers/Suppliers
3.16.2.3 Tier-1 Suppliers (Traditional Pivoting to Software)
3.16.2.4 Chinese OEMs - L2 / NOA Development Timeline
3.16.2.5 Chinese OEMs - L2 / NOA Development
3.16.2.6 Chinese OEMs - Analysis of Sensor Configurations for NOA
3.16.3 ADAS Feature Deployment in EU
3.16.4 ADAS Feature Deployment in Japan
3.17 Global OEM Analysis

4.1 An Overview of DMS and OMS Systems Within In-Cabin Monitoring
4.1.1 Driver Monitoring Systems (DMS)
4.1.2 Occupant Monitoring Systems (OMS)
4.1.2.1 OMS Technology Landscape
4.1.2.2 Radar Emerging as Key OMS Technology
4.1.3 DMS vs. OMS - Market Segmentation
4.1.4 Integration Trends
4.2 Trends of In-Cabin Sensing
4.2.1 Regulatory Mandates Driving Mass Adoption
4.2.1.1 European Union
4.2.1.2 China
4.2.1.3 United States
4.2.2 Transition from Hands-On Detection to Camera-Based DMS
4.2.3 AI and Machine Learning Transforming Capability
4.2.3.1 Emerging AI Capabilities (2024-2026)
4.2.4 Expansion to Full Cabin Monitoring (OMS)
4.2.5 Integration with ADAS and Autonomous Systems
4.2.6 Cost Reduction Through Scale and Integration
4.3 What is a Driver Monitoring System (DMS)?
4.3.1 Core DMS Functions
4.3.2 DMS Technology Stack
4.3.2.1 Hardware Components
4.3.2.2 Software Stack
4.3.3 Why Does the Driver Need Monitoring?
4.3.3.1 The Human Factor in Traffic Safety
4.3.3.2 Specific Driver Impairment Types
4.3.3.3 The Automation Paradox
4.3.3.4 L3 Takeover Challenge
4.3.3.5 Consumer Acceptance and Benefits
4.3.3.6 Regulatory Mandates
4.4 Current Technologies for Interior Monitoring System (IMS)
4.4.1 Technology Classification
4.4.2 Primary Technology Categories
4.4.2.1 Camera-Based Systems:
4.4.3 Driver Monitoring System (DMS)
4.4.3.1 NIR Camera-Based DMS (Dominant Technology)
4.4.3.2 Visible Light Camera-Based DMS (Declining Technology):
4.4.3.3 Steering Torque Sensor-Based DMS (Legacy Technology):
4.4.3.4 Capacitive Steering Wheel DMS
4.4.3.5 Hybrid/Multi-Modal DMS (Emerging Technology)
4.5 In-Cabin Sensing for Autonomous Cars
4.5.1 Level-Specific In-Cabin Sensing Requirements
4.5.1.1 Level 2 (Hands-Off, Eyes-On) - High Monitoring Intensity
4.5.1.2 Level 3 (Conditional Automation) - Critical Monitoring Intensity
4.5.1.3 Level 4 (High Automation) - Reduced but Shifted Monitoring
4.5.1.4 Level 5 (Full Automation) - Passenger Monitoring Only
4.6 Evolution of DMS Sensor Suite From SAE Level 1 to Level 4
4.6.1 Key Technology Transitions
4.7 Emerging Technologies in In-Cabin Sensing
4.7.1 Printed Sensors for Smart Cockpits
4.7.1.1 Human-machine interface (HMI) design printed sensor integration
4.7.1.2 Printed Electronics for Automotive
4.7.1.3 Software to Integrate Smart Cockpit Components
4.7.1.4 Localized Haptics on Cockpit Screens
4.7.1.5 Mid-Air Haptics for Automotive
4.7.1.6 Digital Olfaction for Automotive Use Cases
4.7.2 Alternate Eye Movement Tracking Technologies
4.7.2.1 Eye-Tracking for DMS
4.7.2.2 Eye-Tracking Sensor Categories
4.7.2.3 Eye-Tracking Using Cameras with Machine Vision
4.7.3 Event-Based Vision for Eye-Tracking
4.7.3.1 Eye-Tracking Benefits
4.7.3.2 Event-Based Vision: Pros and Cons
4.7.3.3 Importance of Software for Event-Based Vision
4.7.3.4 Eye Tracking with Laser Scanning MEMS
4.7.3.5 Capacitive Sensing of Eye Movement
4.7.4 Brain Function Monitoring
4.7.4.1 Brain Function Monitoring Technologies
4.7.4.2 Trends in Brain Measurement Technology for Cognitive Workload Monitoring
4.7.4.3 Magnetoencephalography
4.7.4.4 Brain Function Monitoring in the Automotive Space
4.7.4.5 Cardiovascular Metrics
4.7.5 Case Studies and Real World Examples of In-Cabin Sensing Applications
4.7.5.1 BMW iX and X5
4.7.5.2 GM's Super Cruise
4.7.5.3 Polestar 3 Driver Monitoring System
4.7.5.4 Jaguar Land Rover
4.7.5.5 Audi FitDriver
4.7.5.6 MAXUS MIFA 9: DMS Dual OMS
4.7.5.7 Trumpchi GS8
4.7.5.8 Jetour Dashing X90
4.7.5.9 HAVAL - F7
4.7.5.10 WEY - VV6
4.7.5.11 Subaru's DMS
4.7.5.12 Ford - BlueCruise Technology
4.7.5.13 Tesla - IR-Based DMS
4.7.5.14 Tesla In-Cabin Radar
4.7.5.15 Nissan - ProPilot 2.0
4.7.5.16 Toyota and Lexus
4.7.5.17 XPeng Motors
4.7.5.18 Nio ET7 - DMS and OMS Cameras
4.7.5.19 Li Auto L9 - 3D ToF Camera
4.7.5.20 Li Auto - 2D IR Camera for DMS
4.7.5.21 AION
4.7.5.22 Hongqi Auto - Capacitive Steering Wheels Fatigue Detection Cameras
4.8 In-Cabin Sensing market forecasts
4.8.1 Yearly Volume and Market Size of In-Cabin Sensors
4.8.2 Forecast by In-Cabin Sensor Type
4.8.3 Market Share by In-Cabin Sensor Type
4.8.4 Market Share by In-Cabin Imaging Technology
4.8.5 Hands-On Detection (HOD) Sensor Forecast
4.8.6 Regional In-Cabin Sensing Forecasts
4.8.7 Addressable Market by Region (2025-2045)
4.8.8 Addressable Market by SAE Level (2025-2036)

5.1 What is a Software-Defined Vehicle?
5.1.1 Core Characteristics of Software-Defined Vehicles
5.1.2 SDV Market Drivers
5.1.3 SDV Value Chain Transformation
5.1.4 OEM Strategic Imperative
5.1.4.1 Three Strategic Archetypes
5.2 SDV Architecture Evolution
5.2.1 Phase 1: Distributed ECUs (Legacy, Pre-2015)
5.2.2 Phase 2: Domain Controllers (2015-2025)
5.2.3 Phase 3: Zonal Architecture (2023-2030 Transition)
5.2.3.1 Phase 4: Central Compute (2028-2040 Vision)
5.2.4 Key Enabling Technologies
5.2.4.1 Centralized Computing Architecture
5.2.4.2 Over-the-Air (OTA) Update Capability
5.2.4.3 Service-Oriented Architecture (SOA)
5.2.4.4 High-Performance Computing Platforms
5.2.4.5 Connectivity (Always-On Cloud Connection):
5.2.5 Automotive Ethernet - High-Speed Backbone
5.2.5.1 Time-Sensitive Networking (TSN) - Critical Extension
5.2.5.2 Automotive Ethernet Market Sizing
5.2.6 Hypervisors
5.2.6.1 Automotive Hypervisor Requirements
5.2.6.2 Hypervisor Market Sizing
5.2.7 Containerization - Application Portability
5.2.7.1 Containers vs. VMs
5.2.7.2 Automotive Container Technologies
5.2.7.3 Container Use Cases in Automotive
5.2.7.4 Kubernetes for Vehicles
5.2.7.5 Critical Success Factors for SDV Transformation
5.3 Software-Defined Vehicle Level Guide
5.3.1 SDV Maturity Model - Five Levels
5.3.2 SDV Level Chart: Major OEMs Compared
5.3.3 Regional SDV Leadership Patterns
5.3.4 SDV Level 0: Hardware-Defined Vehicle
5.3.5 SDV Level 1: Connected Vehicle - Detailed Analysis
5.3.5.1 Key Enabler: Telematics Control Unit (TCU)
5.3.5.2 Connected Services Enabled
5.3.5.3 Limited OTA Update Capability
5.3.5.4 Architecture Begins to Evolve
5.3.6 SDV Level 2: Domain Controlled Vehicle
5.3.6.1 Extended OTA Capability
5.3.6.2 AUTOSAR Adaptive Platform
5.3.7 SDV Level 3: Centralized Software-Defined Vehicle
5.3.7.1 The Zonal Architecture Transformation
5.3.7.2 Central Compute Platform Architecture
5.3.7.3 Dramatic Wiring Reduction
5.3.7.4 Full Vehicle OTA - All Systems Updatable
5.3.7.5 Third-Party App Ecosystem (Emerging):
5.3.8 SDV Level 4: Fully Software-Defined Vehicle
5.3.8.1 The Ultimate SDV Vision
5.3.8.2 Minimal Hardware Architecture - Central Supercomputing
5.3.8.3 Computing Power Trajectory
5.3.8.4 Hardware Abstraction Benefits
5.3.8.5 Continuous AI/ML Model Updates
5.3.8.6 Cloud-Edge Continuum - Hybrid Computing
5.3.8.7 Vehicle as Edge Node in Smart City
5.3.8.8 Extreme Personalization - AI-Driven
5.3.8.9 Business Model Evolution
5.3.8.10 Level 4 Market Status (2024-2025)
5.3.8.11 Forecast - Level 4 Adoption
5.4 SDV Market Size and Forecast
5.4.1 Geographic Distribution
5.4.2 China
5.4.2.1 Drivers
5.4.2.2 SDV Business Models
5.4.2.3 Challenges
5.4.3 United States
5.4.3.1 US Market Segmentation
5.4.3.2 Drivers and Barriers
5.4.3.3 SDV Business Models:
5.4.4 Europe
5.4.4.1 OEM Strategies
5.4.4.2 European Regulatory Framework
5.4.4.3 European Market Fragmentation
5.4.4.4 SDV Revenue Models
5.4.4.5 European SDV Outlook - 2030 and Beyond
5.4.5 Japan
5.4.5.1 OEM SDV Strategies
5.4.6 SDV Sub-Market Detailed Forecasts
5.4.6.1 Central Compute Platform Market
5.4.6.2 Connected Services Market
5.4.6.3 Subscription vs. One-Time Purchase Models
5.4.6.4 Consumer Acceptance Analysis
5.4.6.5 E/E Architecture Hardware Market - Zone Controller
5.4.6.6 Zone Controller Technology Evolution
5.4.6.7 OTA Software Update Market
5.4.6.8 Software Platform & Middleware Market
5.4.7 Notable Failures and Cautionary Tales
5.5 Personalization and User Profiles
5.5.1 Multi-Dimensional Personalization
5.5.2 Driver Recognition Technologies
5.5.3 Privacy Considerations
5.5.4 Business Value of Personalization
5.6 Autonomous Driving Improvement via Fleet Learning
5.6.1 Fleet Learning Architecture
5.6.2 Economic Model of Fleet Learning
5.6.3 Chinese OEM Fleet Learning Competition
5.6.4 Regulatory and Ethical Considerations
5.7 Vehicle-to-Everything (V2X) Integration
5.7.1 V2X Technology Overview
5.7.2 V2X Technology Standards - Competing Approaches
5.7.3 Economic Impact Analysis
5.7.4 V2X and Autonomous Driving Synergies
5.7.5 Privacy and Security Concerns
5.7.6 V2G Technology
5.7.7 Barriers to V2G Adoption
5.7.8 V2G Forecast
5.8 SDV Feature Layer
5.8.1 SDV Software Stack Architecture
5.8.2 Feature Definition and Categorization
5.8.3 Feature Development Lifecycle in SDV
5.8.4 Feature Monetization Models
5.8.5 Monetization Strategy Evolution
5.8.6 Feature Dependency Mapping
5.9 Generative AI for Software-Defined Vehicles
5.9.1 What is Generative AI?
5.9.1.1 Core Technologies
5.9.2 In-Vehicle Generative AI
5.9.2.1 Smart Cockpit
5.9.2.2 Spike the Personal Assistant (AWS & BMW)
5.9.2.3 A Personalized Digital Assistant (AWS)
5.9.3 Generative AI for Automakers
5.9.3.1 Vizcom (Powered by Nvidia)
5.9.3.2 Microsoft - AI for Automotive
5.9.3.3 Digital Twins and Simulated Autonomy
5.9.4 SDV-Related Regulations
5.10 SDV Competitive Landscape
5.10.1 Tier 1: Technology Leaders
5.10.2 Tier 2: Transitioning Incumbents
5.10.3 Tier-1 Supplier Landscape
5.10.4 Semiconductor Suppliers
5.10.5 Tech Companies Entering Automotive
5.10.6 Business Model Evolution
5.10.6.1 Traditional vs. SDV Business Model Comparison
5.10.6.2 ADAS Subscriptions - The Premium Opportunity
5.10.6.3 Feature Unlocks - One-Time Software Revenue
5.10.6.4 Data Monetization - The Hidden Revenue Stream
5.10.6.5 In-Vehicle Commerce - Emerging Frontier
5.10.6.6 Insurance Telematics - Usage-Based Insurance (UBI)
5.10.7 Competitive Advantage in the ADAS/SDV Era
5.10.8 Strategic Archetypes - Winning Strategies by OEM Type
5.10.9 Critical Strategic Decisions - Framework
5.10.9.1 Vertical Integration vs. Partnerships (Software)
5.10.9.2 Direct Sales vs. Dealer Franchise
5.10.9.3 Geographic Strategy - Global vs. Regional
5.10.9.4 EV Transition Timing
5.10.9.5 Autonomy Strategy - Own vs. Partner vs. Exit
5.11 Consolidation Outlook - Industry Structure 2030-2036
5.12 Supplier Consolidation and Vertical Disintegration
5.12.1 Emerging Supplier Structure

6.1 ADAS Architecture Evolution
6.2 Computing for Camera and Central Platform
6.2.1 Front-Camera Processor Forecast
6.2.2 Central Computing Platform Forecast
6.3 Computing for Radar and LiDAR
6.3.1 Radar Processing Forecast
6.3.2 LiDAR Processing Forecast
6.3.3 ADAS Processor Volume Forecast (2024-2030)
6.4 ADAS Processor ASP Analysis
6.5 ADAS Processor Revenue Forecast (2024-2030)
6.6 Computing for Infotainment and Telematics
6.7 Processor Wafer Production Forecast

7.1 Passenger Car & Light Commercial Vehicle (PC & LCV) LiDAR Market Forecast
7.2 Regional Breakdown
7.3 OEM Adoption Tiers
7.4 Robotaxi LiDAR Market Forecast
7.4.1 Robotaxi Operator Strategies
7.4.2 Robotaxi LiDAR Market Concentration
7.5 LiDAR Deployment Trends
7.6 LiDAR Performance Trends
7.7 LiDAR Camera Fusion Architectures
7.8 LiDAR is for Level 3 and the Chinese Market
7.9 Automotive LiDAR Players by Technology

8.1 Connected Vehicle Market Overview and Penetration Forecast
8.1.1 Definition and Scope
8.1.2 Connected Vehicle Use Cases and Revenue Streams
8.1.3 Regional Connected Vehicle Penetration
8.2 V2X Technology Competition - C-V2X vs. DSRC
8.3 V2X Deployment Forecast and Infrastructure Buildout
8.3.1 Regional V2X Deployment Dynamics
8.4 V2X Use Cases and Value Proposition
8.4.1 V2X Efficiency Use Cases (Traffic Management)
8.5 V2X Business Models and Funding Challenges
8.6 V2X Chipset and Equipment Market Forecast
8.6.1 Competitive Landscape
8.7 Autonomous Vehicle Coordination via V2X - The "Killer App"?
8.8 V2X Market Outlook

9.1 Cockpit Processor Evolution
9.1.1 Multi-Display Support (4-6 Screens)
9.1.2 Display Rendering Challenges
9.1.3 GPU Performance Requirements
9.1.4 GPU Architecture Trends
9.1.5 AI NPU Integration
9.1.6 Automotive AI Workloads (Cockpit)
9.1.7 Virtualization and Hypervisors
9.2 AI Assistant Technologies
9.2.1 Voice Recognition Improvements
9.2.2 Technology Drivers
9.2.3 On-Device vs. Cloud ASR
9.2.4 Generative AI Integration
9.2.5 Large Language Model (LLM) Deployment
9.2.6 Deployment Modes
9.2.7 Edge vs. Cloud Processing
9.3 Display Technologies
9.3.1 OLED and Mini-LED Adoption
9.3.2 OLED Automotive Advantages
9.3.3 OLED Challenges
9.3.4 Mini-LED Adoption Trajectory
9.3.5 Flexible and Curved Displays
9.3.5.1 Flexible OLED Challenges (Automotive-Specific)
9.3.6 Augmented Reality HUD
9.3.7 AR-HUD Challenges
9.4 Connectivity Integration
9.4.1 5G Deployment
9.4.2 5G Modem Penetration
9.4.3 V2X Communication
9.4.4 Edge Computing

10.1 What is Localization?
10.1.1 Localization: Absolute vs Relative
10.1.2 Lane Models: Uses and Shortcomings
10.2 HD Mapping Assets: From ADAS Map to Full Maps for Level-5 Autonomy
10.3 Many Layers of an HD Map for Autonomous Driving
10.4 HD Map as a Service
10.5 Mapping Business Models
10.6 HD Mapping with Cameras
10.7 Teleoperation
10.7.1 Enabling Autonomous MaaS
10.7.2 Three Levels of Teleoperation
10.7.3 Where is Teleoperation Currently Used?

Table 1. Global Automotive Technology Market Summary (2024-2030)
Table 2. Architecture Evolution Timeline
Table 3. Autonomous Feature Adoption Forecast Summary
Table 4. Regional Market Summary 2024-2030
Table 5. Teleoperation Approaches - Comparison
Table 6. Camera, Radar, LiDAR - Core Capabilities Comparison
Table 7. LiDAR Technology Comparison (2024)
Table 8. Sensor Suite Evolution Across Autonomy Levels
Table 9. Sensor Fusion Architecture Comparison
Table 10. Autonomy System Power Consumption Breakdown (L4 Robotaxi)
Table 11. Autonomy Impact on EV Range
Table 12. Detection Challenges - Vehicles vs. Vulnerable Road Users
Table 13. Pedestrian Risk Assessment Matrix
Table 14. Sensor Suite Evolution by Autonomy Level
Table 15. Automotive Camera Specifications by Position (2024)
Table 16. Thermal Camera Advantages and Limitations
Table 17.Automotive Radar Specifications (2024 State-of-the-Art)
Table 18. LiDAR Wavelength Comparison
Table 19. LiDAR Scanning Technologies Comparison (2024)
Table 20. ADAS Technology Evolution Waves
Table 21. SAE Levels of Driving Automation - Detailed Breakdown
Table 22. SAE Automation Levels - Official Definitions vs. Market Reality
Table 23. Autonomous Vehicle Hype vs. Reality Timeline
Table 24. L2 System Compute Requirements
Table 25. Level 2 System Comparison - Major OEMs
Table 26. Level 2 Deployment Status by Major System
Table 27. L2 Subscription Models (2024)
Table 28. L2 vs. L2 Feature Comparison
Table 29. L2 Penetration Forecast by Region (2024-2035)
Table 30. L2 vs. L3 Capability Gap (2020 vs. 2024 vs. 2030 Projection)
Table 31. Level 3 System ODD Restrictions
Table 32. OEM Automation Strategy (2024-2025)
Table 33. Level 3 Regulatory Status by Region/Country (2024-2025)
Table 34. Liability by Automation Level
Table 35. OEM L3 Strategies (2024)
Table 36. Major Robotaxi Operations (Q4 2024 - Q1 2025)
Table 37. ADAS Feature Penetration Rates - Global New Vehicle Sales
Table 38. Comprehensive L2 and L2 Systems by Manufacturer
Table 39. Chinese L2 Urban NOA Capability Comparison (2024-2025)
Table 40. ADAS Feature Classification and Penetration (2024)
Table 41. AEB System Performance (2024 State-of-the-Art)
Table 42. ACC Feature Levels
Table 43. LKA Capability by Road Condition
Table 44. Blind Spot Monitoring Variants
Table 45. Traffic Sign Recognition Performance (2024 State-of-the-Art)
Table 46. 360° Camera Feature Levels
Table 47. Automated Parking Feature Progression
Table 48. Automated Parking Penetration Forecast (2024-2035)
Table 49. Global ADAS Feature Penetration Snapshot (2023 vs. 2024)
Table 50. SAE Level 2 Adoption Growth (2020-2024)
Table 51. L2 Penetration by Region (2022-2024, % of New Vehicle Sales)
Table 52. Average Sensor Count by SAE Level (2024 Global Average)
Table 53. Automotive LiDAR Penetration by Region (2024)
Table 54. Automotive LiDAR Market Forecast (2024-2030)
Table 55. Advanced ADAS Feature Penetration - Global (% of New Vehicle Sales)
Table 56. ADAS Feature Penetration - United States (% of New Vehicle Sales)
Table 57. ADAS Feature Penetration - China (% of New Vehicle Sales)
Table 58. ADAS Feature Penetration - Japan (% of New Vehicle Sales)
Table 59. Global Vehicle Sales by SAE Level (2022-2045, Millions of Units)
Table 60. L3 Regulatory Approval Timeline
Table 61. Vehicle Sales Peak Timing by Market Development
Table 62. ADAS Market Implications from Vehicle Sales Trajectory
Table 63. Multi-Sensor Fusion vs. Pure Vision - Comparative Analysis (2024)
Table 64. Autonomous Driving Architecture Comparison
Table 65. E2E Benefits vs. Modular Systems
Table 66. E2E Challenges and Risks
Table 67. E2E Deployment Status - Global Landscape (2024)
Table 68. Tesla FSD v12 (E2E) Performance Progression (2024)
Table 69. OEM Reluctance to E2E - Reasons
Table 70. Sensor Suite Evolution by Automation Level (Typical Configurations)
Table 71. Sensor Suite Cost by Automation Level (2024 Hardware Cost)
Table 72. ADAS Sensor Types - Capabilities and Limitations
Table 73. Sensor Strengths by Scenario
Table 74. Representative Sensor Suites by Level (Detailed Breakdown)
Table 75. Baseline L2 Sensor Suite (Honda Civic, Toyota Camry, VW Jetta Tier)
Table 76. Integrated Front Camera Architectures (2024)
Table 77. Global Camera-Based ADAS Regulations (2024-2025)
Table 78. Tier-1 Front Camera Suppliers - Product Portfolio (2024-2025)
Table 79. Specialized Camera Suppliers (2024)
Table 80. External Camera Locations and Functions
Table 81. Front Radar Specifications and Applications
Table 82. Side Radar Configuration and Coverage (L2 System)
Table 83. Radar Count by Automation Level
Table 84. Radar Adoption by Region and Level (2024)
Table 85. 4D Imaging Radar vs. Traditional Radar vs. LiDAR
Table 86. 4D Imaging Radar Suppliers (2024-2025)
Table 87. Automotive LiDAR Adoption by Region and OEM Strategy (2024)
Table 88. Automotive LiDAR Technologies - Comparison
Table 89. Top Automotive LiDAR Suppliers - Market Positioning (2024)
Table 90. Automotive LiDAR Cost Evolution (Historical and Projected)
Table 91. Top 4 Chinese LiDAR Manufacturers - Market Analysis (2024)
Table 92. Chinese vs. Western LiDAR - Competitive Dynamics
Table 93. Tier-1 Supplier ADAS Challenges - 2023-2024 Crisis
Table 94. Tier-1 ADAS Strategy Pivots
Table 95. Major Tier-1 ADAS Product Offerings (2025)
Table 96. Regional L2 /L3 Adoption Comparison (2030 Forecast)
Table 97. United States - Autonomous Vehicle Sales by SAE Level (2022-2045)
Table 98. US L2 /L3 Adoption by State/Region (2030 Forecast)
Table 99.United States - ADAS Feature Revenue Forecast (2024-2030, USD Millions)
Table 100. US ADAS Market Summary (2024-2030)
Table 101. China - Autonomous Vehicle Sales by SAE Level (2022-2044)
Table 102. Major Chinese OEM L2 Systems (2024 Deployment)
Table 103. Chinese Urban Commute Characteristics vs. US/Europe
Table 104. China L2 /L3 Adoption by Tier City (2030 Forecast)
Table 105. China Domestic ADAS Supply Chain vs. Western Dependence
Table 106. Europe - Autonomous Vehicle Sales by SAE Level (2022-2044)
Table 107. EU GSR Mandatory ADAS Features Timeline
Table 108. Euro NCAP Protocol Evolution - ADAS Requirements
Table 109. European L2 Deployment by Use Case (2024 & 2030 Forecast)
Table 110. Japan - Autonomous Vehicle Sales by SAE Level (2022-2044)
Table 111. Japan ADAS Adoption Barriers
Table 112. Non-Japanese Brand Market Share in Japan (2024 & 2030E)
Table 113. Regional ADAS Penetration Comparison (2023 Production Vehicles)
Table 114. US ADAS Feature Penetration by Vehicle Segment (2023)
Table 115. China ADAS Feature Penetration by Vehicle Segment (2023)
Table 116. China ADAS SoC (System-on-Chip) Landscape (2023-2024)
Table 117. China NOA Solution Providers (2024)
Table 118. Chinese Tier-1 Suppliers - ADAS Positioning (2024)
Table 119. Major Chinese OEM L2 /NOA Deployment Timeline (2020-2025)
Table 120. Chinese OEM ADAS Development Strategy Comparison (2024)
Table 121. Chinese Premium OEM Sensor Configurations - Urban NOA Systems (2024)
Table 122. EU ADAS Feature Penetration by Vehicle Segment (2023)
Table 123. Japan ADAS Feature Penetration by Vehicle Segment (2023)
Table 124. US Regulatory Environment - NHTSA Stance
Table 125. Global OEM L2 /NOA Strategies - Comprehensive Comparison (2024)
Table 126. Driver Monitoring System Capabilities and Applications
Table 127. Occupant Monitoring System Capabilities and Applications
Table 128. OMS Technologies by Function
Table 129. DMS vs. OMS Market Comparison (2024-2036)
Table 130. EU GSR In-Cabin Monitoring Requirements
Table 131. Global DMS Regulatory Status Summary (2024-2025)
Table 132. Transition from Torque-Based to Camera-Based DMS (2020-2030)
Table 133. AI/ML Performance Gains in DMS (Typical Systems)
Table 134. DMS/OMS Integration with ADAS Levels
Table 135. DMS System Cost Evolution (2020-2030)
Table 136. DMS Hardware Components
Table 137. DMS Software Components
Table 138. Traffic Accident Causation Analysis
Table 139. The ADAS Monitoring Paradox
Table 140. DMS Benefits from Consumer Perspective
Table 141. IMS Technology Classification Framework
Table 142. Typical NIR DMS Camera Specifications
Table 143. ToF Camera Market Forecast - In-Cabin Applications
Table 144. DMS Technology Categories
Table 145. NIR Camera DMS Performance Metrics
Table 146. Leading NIR Camera-Based DMS Suppliers
Table 147. NIR Camera-Based DMS Market Forecast (2024-2036)
Table 148. Multi-Modal DMS Performance Improvements
Table 149. In-Cabin Monitoring Intensity by Automation Level
Table 150. L2 System Response to DMS Detections
Table 151. L2 DMS Real-World Intervention Rates
Table 152. L3-Specific In-Cabin Monitoring Requirements
Table 153. L3 DMS Redundancy Approaches
Table 154. L3 Takeover Research Summary
Table 155. L4 Robotaxi In-Cabin Monitoring Requirements
Table 156. DMS/OMS Sensor Suite Evolution by SAE Level
Table 157. Eye-Tracking Technologies for Automotive DMS
Table 158. DMS Camera System Suppliers (2024)
Table 159. Event-Based Vision vs. Traditional Cameras - Eye-Tracking Performance
Table 160. Capacitive Eye-Tracking vs. Camera DMS
Table 161. Brain Monitoring Technologies - Overview
Table 162. Automotive Brain Monitoring Research Projects
Table 163. Steering Wheel ECG Challenges
Table 164. In-Cabin Sensing Market Overview (2020-2036)
Table 165. In-Cabin Sensor Volume Forecast by Type (Millions of Units)
Table 166. In-Cabin Sensor Market Size by Type (USD Millions)
Table 167. Technology Market Share Evolution (% of Total In-Cabin Sensing Revenue)
Table 168. Camera Technology Split (% of In-Cabin Camera Revenue)
Table 169. HOD Sensor Market Forecast
Table 170. In-Cabin Sensing Market by Region (USD Millions)
Table 171. Long-Term In-Cabin Sensing Addressable Market (2045)
Table 172. In-Cabin Sensing Requirements by Autonomy Level
Table 173. SDV Defining Characteristics
Table 174. Automotive Value Chain - Traditional vs. SDV
Table 175. Domain Controller Market Size (2024-2030)
Table 176. Zonal Architecture vs. Domain Architecture - Comparison
Table 177. E/E Architecture Penetration Forecast (Global, 2024-2035)
Table 178. Traditional vs. SDV Computing Architecture
Table 179. OTA Update Capability Levels
Table 180. Example SDV Service Layers
Table 181. Computing Power Evolution in Vehicles
Table 182. Automotive Network Technology Comparison
Table 183. Automotive Ethernet Standards Timeline
Table 184. TSN Standards for Automotive
Table 185. Automotive Ethernet Market Forecast (2024-2035, USD Millions)
Table 186. Hypervisor Types for Automotive
Table 187. Hypervisor Deployment Scenarios
Table 188. Automotive Hypervisor Market Forecast (2024-2035, USD Millions)
Table 189. Virtual Machines vs. Containers
Table 190. Automotive Container Runtime Landscape
Table 191. Automotive Containerization Market Forecast (2024-2035, USD Millions)
Table 192. Traditional vs. SDV Development Processes
Table 193. SDV Cybersecurity Requirements (ISO/SAE 21434)
Table 194. SDV Cloud Infrastructure Needs
Table 195. Software-Defined Vehicle Level Definitions
Table 196. Major OEMs - SDV Level Assessment (2024-2025)
Table 197. Regional SDV Leadership Assessment
Table 198. Typical Level 0 Vehicle ECU Distribution (Example: 2010 Premium Sedan)
Table 199. Level 0 Vehicle Communication Networks
Table 200. Level 0 Business Model Characteristics
Table 201. Telematics Control Unit Components and Functions
Table 202. Typical Level 1 Connected Services
Table 203. Level 1 Vehicle Data Collection
Table 204. Level 1 Business Model Changes vs. Level 0
Table 205. Typical Level 2 Domain Architecture
Table 206. Level 2 Communication Network Architecture
Table 207. Level 2 OTA Update Scope
Table 208. Wiring Harness Comparison - Level 0 vs. Level 2
Table 209. Level 2 Subscription Service Examples
Table 210. Typical Level 3 Zonal Architecture
Table 211. Central Compute Platform Configuration (Typical Level 3 Vehicle)
Table 212. Wiring Harness Evolution - Level 0 to Level 3
Table 213. Level 3 OTA Update Scope - Comprehensive
Table 214. Level 3 Features on Demand - Examples
Table 215. Level 4 Architecture - Extreme Centralization
Table 216. Vehicle Computing Power Evolution - Historical to Level 4
Table 217. Hardware Abstraction Layer (HAL) Benefits
Table 218. Level 4 Continuous AI/ML Update Pipeline
Table 219. Cloud vs. Edge Compute in Level 4 Vehicles
Table 220. Vehicle-to-Infrastructure (V2I) Integration in Level 4
Table 221. Level 4 AI-Driven Personalization Examples
Table 222. Level 4 Recurring Revenue Model - Comprehensive
Table 223. Level 4 SDV Market Penetration Forecast (2024-2036)
Table 224. SDV Market Segmentation Framework
Table 225. SDV Market by Geography (2024 vs. 2030 vs. 2036)
Table 226. China SDV Adoption Forecast (2024-2035)
Table 227. Chinese OEM SDV Strategies (2024)
Table 228. SDV Component Cost Comparison - China vs. Western
Table 229. United States SDV Adoption Forecast (2024-2035)
Table 230. Europe SDV Adoption Forecast (2024-2035)
Table 231. Cariad Failure Analysis
Table 232. European Consumer SDV Attitudes (2024 Survey Data)
Table 233. SDV Adoption Forecast (2024-2035)
Table 234. Japanese Consumer SDV Attitudes (2024 Survey)
Table 235. Regional SDV Adoption - Comparative Summary (2030 Projections)
Table 236. Central Compute Platform Market Forecast (2024-2036)
Table 237. Connected Services Market Forecast by Category (2024-2036)
Table 238. Subscription vs. One-Time Purchase - Market Split and Evolution
Table 239. Consumer Willingness to Pay for Connected Services (Survey Data)
Table 240. Zone Controller Market Forecast (2024-2036)
Table 241. Zone Controller Specifications - Evolution
Table 242. OTA Software Update Market Forecast (2024-2036)
Table 243. OTA Cost Breakdown per Vehicle per Year
Table 244. OTA Platform Strategy by OEM Type
Table 245. Automotive Software Platform Market Forecast (2024-2036)
Table 246. Automotive Operating System Market Share and Trends
Table 247. SDV Challenges and Setbacks (2020-2024)
Table 248. Vehicle Personalization Dimensions in SDV
Table 249. Driver Identification Technologies - Comparison
Table 250. Personalization Privacy Framework
Table 251. Personalization Business Value to OEMs
Table 252. Fleet Learning Pipeline - Step-by-Step
Table 253. Fleet Learning Economic Flywheel
Table 254. Chinese OEM Fleet Learning Comparison (2024)
Table 255. Fleet Learning Regulatory and Ethical Issues
Table 256. V2X Communication Types - Comprehensive Taxonomy
Table 257. V2X Technology Standards Comparison
Table 258. V2X Economic Impact Estimates (US DOT and EU Studies)
Table 259. V2X Contribution to Autonomous Driving
Table 260. V2X Privacy and Security Considerations
Table 261. V2G Applications and Value
Table 262. V2G Deployment Barriers
Table 263. V2G Market Penetration Forecast
Table 264. SDV Software Stack - Complete Architecture
Table 265. SDV Feature Taxonomy - Comprehensive Classification
Table 266. Feature Development Lifecycle - Traditional vs. SDV
Table 267. Feature Monetization Models - Detailed Analysis
Table 268. OEM Feature Monetization Maturity Stages
Table 269. Feature Dependency Matrix - Example Features
Table 270. OEM SDV Competitive Tiers (2024)
Table 271. Tier-1 Supplier SDV Positioning (2024)
Table 272. Automotive Semiconductor Winners (SDV Era)
Table 273. Automotive Business Model Evolution
Table 274. Automotive Recurring Revenue Streams Forecast (2024-2035, USD Billions)
Table 275. ADAS Subscription Market by Level (2030 Projection)
Table 276. Feature Unlock Categories and Pricing (2024)
Table 277. Vehicle Data Categories and Monetization Opportunities
Table 278. In-Vehicle Commerce Categories
Table 279. Competitive Advantage Evolution
Table 280. OEM Strategic Archetypes
Table 281. Geographic Strategy Matrix
Table 282. EV Transition Strategy
Table 283. Autonomy Strategy Options
Table 284. Automotive Supplier Value Chain - 2024 vs. 2035
Table 285. ADAS Architecture Adoption Forecast (% of Global New Vehicle Production)
Table 286. Front-Camera Processor Market Forecast (2024-2030)
Table 287. Central Computing Platform Market Forecast (2024-2030)
Table 288. Radar Processing Market Forecast (2024-2030)
Table 289. LiDAR Processing Market Forecast (2024-2030)
Table 290. ADAS Processor Unit Volume Forecast by Application (Millions of Units)
Table 291. ADAS Processor Volume by Autonomy Level (Millions of Vehicles)
Table 292. ADAS Processor Volume by Region (Millions of Units)
Table 293. ADAS Processor ASP Trends by Application
Table 294. ADAS Processor Market Revenue Forecast by Application (USD Billions)
Table 295. Total Automotive Processor Market (ADAS Infotainment)
Table 296. Infotainment Processor Market Summary
Table 297. Automotive Processor Wafer Demand by Technology Node (Thousands of 300mm Wafer Equivalents/Year)
Table 298. Global PC & LCV LiDAR Market Forecast (2024-2035)
Table 299. LiDAR-Equipped Vehicle Forecast by Region (2024-2035)
Table 300. OEM LiDAR Strategy Segmentation (2024)
Table 301. Robotaxi LiDAR Market Forecast (2024-2035)
Table 302. Robotaxi LiDAR Supplier Market Share (2024)
Table 303. LiDAR Placement and Integration Trends
Table 304. Automotive LiDAR Performance Evolution (2020-2035)
Table 305. LiDAR/Camera Fusion Strategies
Table 306. LiDAR Penetration by ADAS Level (2024)
Table 307. LiDAR Technology Comparison
Table 308. LiDAR Supplier Outlook (2024 ? 2030)
Table 309. Global Connected Vehicle Penetration Forecast (2024-2035)
Table 310. Connected Vehicle Applications and Monetization (2024)
Table 311. Connected Vehicle Penetration by Region (2024 & 2030)
Table 312. DSRC vs. C-V2X Technical Comparison
Table 313. C-V2X Vehicle and Infrastructure Deployment Forecast (2024-2035)
Table 314. Regional C-V2X Deployment (2024 & 2030)
Table 315. V2X Communication Modes and Use Cases
Table 316. V2X Safety Applications and Impact
Table 317. V2X Efficiency Applications
Table 318. V2X Funding Models by Region
Table 319. V2X Chipset Market Forecast (2024-2035, USD Millions)
Table 320. V2X Chipset Supplier Market Share (2024)
Table 321. V2X for Autonomous Vehicles - Hype vs. Reality
Table 322. Cockpit Processor Evolution Timeline (2015-2025)
Table 323. Multi-Display Cockpit Configurations (2024 Examples)
Table 324. GPU Performance Demand - Automotive Cockpit (2015 vs. 2024)
Table 325. Cockpit AI Workloads and NPU Requirements
Table 326. Automotive Hypervisors - Market Overview (2024)
Table 327. Automotive Voice Assistant Evolution (2015-2025)
Table 328. Automotive ASR Accuracy (Word Error Rate - WER)
Table 329. On-Device vs. Cloud ASR Trade-Offs
Table 330. Generative AI Automotive Use Cases (2024-2025)
Table 331. LLM Deployment Architectures - Automotive (2024)
Table 332. Automotive Display Technologies (2024)
Table 333. Automotive Display Technology Forecast (2024-2030)
Table 334. Flexible Display Use Cases - Automotive
Table 335. HUD Technology Generations
Table 336. AR-HUD Challenges and Current Solutions
Table 337. 5G Automotive Applications (2024)
Table 338. Automotive 5G Modem Adoption (2024-2030)
Table 339. Automotive Edge Computing Tiers
Table 340. HD Mapping Providers - Market Overview (2024)
Table 341. Teleoperation Solution Providers (2024)

Figure 1. How ADAS works
Figure 2. Smart Car with ADAS sensors
Figure 3. ADAS component packaging
Figure 4. Sensor configuration diagrams for typical L2 systems
Figure 5. L3 system architecture
Figure 6. Autonomous Driving Feature Evolution Timeline
Figure 7. North America ADAS Feature Roadmap
Figure 8. Europe ADAS Feature Roadmap
Figure 9. China ADAS Feature Roadmap
Figure 10. Japan ADAS Feature Roadmap
Figure 11. Automotive LiDAR Market Forecast (2024-2030)
Figure 12. Global Vehicle Sales by SAE Level (2022-2045, Millions of Units)
Figure 13. Sensor Count vs. Automation Level (Industry Average)
Figure 14. United States - Autonomous Vehicle Sales by SAE Level (2022-2045)
Figure 15. United States - ADAS Feature Revenue Forecast (2024-2030, USD Millions)
Figure 16. Comparison images showing visible vs. NIR camera view of driver in various lighting conditions
Figure 17. Waymo robotaxi interior showing camera coverage zones and monitoring functions
Figure 18. In-Cabin Sensing Market Overview (2020-2036)
Figure 19. In-Cabin Sensor Volume Forecast by Type (Millions of Units)
Figure 20. In-Cabin Sensor Market Size by Type (USD Millions)
Figure 21. Visual progression showing E/E architecture evolution from Level 0 to Level 4 with simplified vehicle electrical architecture diagrams
Figure 22. SOA Example - Simplified
Figure 23. SDV software stack diagram showing layers from hardware (bottom) to features (top) with bidirectional arrows showing service calls
Figure 24. Central Computing Platform Market Forecast (2024-2030)
Figure 25. Radar Processing Market Forecast (2024-2030)
Figure 26. LiDAR Processing Market Forecast (2024-2030)
Figure 27. ADAS Processor Unit Volume Forecast by Application (Millions of Units)
Figure 28. ADAS Processor Volume by Autonomy Level (Millions of Vehicles)
Figure 29. ADAS Processor Volume by Region (Millions of Units)
Figure 30. ADAS Processor Market Revenue Forecast by Application (USD Billions)
Figure 31. Total Automotive Processor Market (ADAS Infotainment)
Figure 32. Automotive Processor Wafer Demand by Technology Node (Thousands of 300mm Wafer Equivalents/Year)