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市场调查报告书
商品编码
2021743

高阶驾驶辅助系统 (ADAS) 的人工智慧 (AI) 市场：未来预测（至 2034 年）—按组件、技术、自动驾驶等级、车辆类型、动力系统、应用和地区进行分析

AI in ADAS Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software, and Services), Technology, Level of Autonomy, Vehicle Type, Propulsion Type, Application and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，到 2026 年，全球 ADAS 人工智慧市场规模将达到 120 亿美元，预计在预测期内将以 24.8% 的复合年增长率增长，到 2034 年将达到 700 亿美元。

人工智慧在高级驾驶辅助系统 (ADAS) 中的应用，融合了智慧演算法和机器学习技术，旨在提升车辆安全性、驾驶效率和自动化程度。这些系统分析来自感测器、摄影机和雷达的即时数据，以侦测障碍物、识别交通标誌、监控驾驶员行为并辅助决策。人工智慧支援车道维持辅助、主动式车距维持定速系统和碰撞避免等功能，从而减少人为错误，改善整体驾驶体验，并加速迈向完全自动驾驶汽车的进程。

严格的车辆安全法规和NCAP要求

全球各国政府和汽车安全机构都在强制要求新车配备高级驾驶辅助系统（ADAS）。美国国家公路交通安全管理局（NHTSA）和欧洲新车安全评估协会（Euro NCAP）等监管机构要求车辆必须具备自动紧急煞车、车道偏离预警和行人侦测等功能才能获得高安全评级。这些法规迫使汽车製造商将人工智慧驱动的ADAS整合到车辆中。此外，消费者道路安全意识的提高以及配备ADAS车辆的保险优惠政策也进一步加速了ADAS的普及。随着全球安全标准的日益严格，汽车製造商被迫增加对基于人工智慧的感知和决策演算法的投资。这种监管压力直接推动了对先进ADAS硬体和软体的需求，使其成为市场成长的主要催化剂。

开发和检验人工智慧系统的成本很高。

为高阶驾驶辅助系统 (ADAS) 开发人工智慧模型需要大量的标註资料集、高效能运算基础设施和广泛的现场测试。在各种天气、光照和交通条件下进行系统检验既耗时又昂贵。汽车製造商还必须遵守 ISO 26262 等功能安全标准，这增加了软体开发的复杂性和成本。这些前期投资可能成为二级和三级供应商的障碍，从而限制其市场参与企业。此外，空中升级和网路安全措施也会产生持续的成本。中小型汽车製造商和售后市场公司往往难以负担这些成本，阻碍了技术的广泛应用。因此，高昂的开发和认证成本仍然是 ADAS 人工智慧市场的主要阻碍因素。

电动车和自动驾驶汽车的快速发展

电动车依赖高效的能源管理，而人工智慧驱动的高级驾驶辅助系统（ADAS）可实现能量回收煞车和优化路线规划。同时，无人驾驶计程车和L4级自动驾驶接驳车的开发需要先进的感测器融合和边缘人工智慧技术。汽车製造商正与人工智慧晶片製造商和软体公司建立策略合作伙伴关係，以加速这些技术的应用。此外，政府对智慧城市基础设施和自动驾驶车辆测试车道的投入也支持了这一成长。随着消费者对自动驾驶功能的信心不断增强，人工智慧驱动的ADAS将在大众市场中得到更广泛的应用。电气化和自动化的整合将为技术提供者和汽车製造商创造新的收入来源。

与网路安全漏洞和感测器可靠性相关的挑战

人工智慧驱动的高级驾驶辅助系统（ADAS）高度依赖外部感测器和网路连接，因此极易受到网路攻击，例如感测器欺骗、GPS干扰以及操纵物体识别的对抗性人工智慧攻击。一旦ADAS系统遭到入侵，可能导致误煞车、转向失灵，甚至系统完全失效，进而危及人身安全。此外，现有感测器易受暴雨、雾霾、阳光直射和灰尘堆积等不利条件的影响，这些都会降低人工智慧模型的精确度。光达和摄影机的时变性也会进一步降低可靠性。如果没有强大的故障保护机制和即时异常侦测，这些漏洞将威胁到消费者的接受度。汽车製造商必须在冗余、加密和反欺骗技术方面投入大量资金。在这些威胁彻底消除之前，具备进阶自动驾驶功能的ADAS的广泛应用仍面临风险。

新冠疫情的影响：

新冠疫情透过半导体短缺、工厂停工和汽车产量下降，对ADAS（高级驾驶辅助系统）的人工智慧市场造成了衝击。供应链瓶颈延缓了配备ADAS的新车型上市，尤其是在中阶市场。然而，疫情也加速了人们对非接触式移动和健康驾驶的需求，使自动代客泊车和车载空气品质监测等功能备受关注。此外，ADAS也应用于物流和配送车辆，以提升最后一公里配送的安全性。随着汽车生产的復苏，汽车製造商正优先考虑ADAS的集成，以满足尚未落实的安全法规要求。此次危机也促使汽车製造商实现感测器在地化生产，并建构更完善的人工智慧开发平臺，增强了市场的长期前景。

在预测期内，硬体领域预计将占据最大份额。

在预测期内，硬体部分预计将占据最大的市场份额。该部分包括摄影机、雷达感测器、光达感测器、超音波感测器和电控系统，它们构成了高级驾驶辅助系统（ADAS）的物理基础。入门级和高端车辆对高解析度成像、远距离探测和即时处理的需求，推动了硬体部分的领先地位。固态雷射雷达和4D成像雷达的持续进步，也进一步提升了对硬体的需求。

预计在预测期内，边缘人工智慧领域将呈现最高的复合年增长率。

在预测期内，边缘人工智慧系统领域预计将呈现最高的成长率。边缘人工智慧在车辆晶片本地处理数据，从而降低延迟并减少对云端连接的依赖。这对于诸如自动紧急煞车等即时高级驾驶辅助系统（ADAS）功能至关重要。专为汽车开发的人工智慧加速器，例如神经处理单元（NPU），在提高设备推理速度的同时，也能降低功耗。边缘人工智慧也透过最大限度地减少资料传输到外部来源，从而有助于提高资料隐私性。

市占率最大的地区：

在预测期内，北美预计将占据最大的市场份额。这主要得益于特斯拉、通用、福特以及英伟达和英特尔旗下Mobileye等ADAS晶片供应商的强大市场地位。消费者对先进安全功能的高度认可、美国国家公路交通安全管理局（NHTSA）的严格监管以及半自动驾驶技术的早期应用，都推动了市场成长。此外，该地区也集中了许多主要的ADAS软体开发中心。成熟的电动车（EV）生态系统以及对自动驾驶出行服务的巨额投资，也巩固了北美在全球ADAS人工智慧市场的领先地位。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于中国、日本和韩国汽车电气化的快速发展。印度和东南亚政府对安全技术的强制性要求，以及雷射雷达和摄影机生产的积极本地化，将降低系统成本。比亚迪和蔚来等中国汽车製造商正在将先进的人工智慧模型融入其量产车型。自动驾驶测试区域的扩展和智慧基础设施专案的推进将进一步加速人工智慧技术的应用。随着车辆数量的增加和安全意识的提高，亚太地区将成为高级驾驶辅助系统（ADAS）人工智慧市场成长最快的地区。

免费客製化服务：

订阅本报告的用户可享有以下免费自订选项之一：

  • 公司简介
    • 对其他公司（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣量身定制的主要国家/地区的市场估算、预测和复合年增长率（註：基于可行性检查）
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
  • 分析材料

第三章 市场趋势分析

  • 促进因素
  • 抑制因子
  • 机会
  • 威胁
  • 产品分析
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • 新冠疫情的影响

第四章：波特五力分析

  • 供应商议价能力
  • 买方的议价能力
  • 替代产品的威胁
  • 新进入者的威胁
  • 竞争公司之间的竞争

第五章：全球ADAS人工智慧市场：按组件划分

  • 硬体
    • 相机
    • 超音波感测器
    • 雷达感测器
    • 电控系统（ECU）
    • 光达感测器
  • 软体
    • AI中介软体
    • 融合与决策演算法
    • 感知软体
  • 服务
    • 整合与部署
    • 培训和支持

第六章：全球ADAS人工智慧市场：按技术划分

  • 机器学习（ML）
  • 边缘人工智慧
  • 深度学习（DL）
  • 感测器融合
  • 电脑视觉
  • 自然语言处理（NLP）

第七章：全球ADAS人工智慧市场：按自动驾驶等级划分

  • L1（驾驶辅助）
  • L2（部分自动驾驶）
  • L3（有条件自动驾驶）
  • L4（高度自动化）
  • L5（完全自动驾驶）

第八章：全球ADAS人工智慧市场：按车辆类型划分

  • 搭乘用车
  • 大型商用车辆
  • 轻型商用车

第九章：全球ADAS人工智慧市场：按实施方法划分

  • 内燃机车
  • 混合动力汽车
  • 电动车（EV）

第十章：全球ADAS人工智慧市场：按应用领域划分

  • 主动式车距维持定速系统（ACC）
  • 车道维持辅助系统（LKA）
  • 环景显示系统
  • 自动紧急煞车（AEB）
  • 驾驶员监控系统（DMS）
  • 盲点侦测（BSD）
  • 交通标誌识别（TSR）
  • 停车协助

第十一章 全球ADAS人工智慧市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 其他地区（ROW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十二章 策略市场资讯

  • 产业加值网络与供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十三章 产业趋势与策略倡议

  • 企业合併（M&A）
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十四章：公司简介

  • Tesla, Inc.
  • NVIDIA Corporation
  • Intel Corporation
  • Qualcomm Incorporated
  • Robert Bosch GmbH
  • Continental AG
  • ZF Friedrichshafen AG
  • Aptiv PLC
  • Valeo SA
  • Hyundai Mobis
  • Denso Corporation
  • Ambarella, Inc.
  • Horizon Robotics
  • Seeing Machines Ltd.
  • Plus.ai
Product Code: SMRC35023

According to Stratistics MRC, the Global AI in ADAS Market is accounted for $12.0 billion in 2026 and is expected to reach $70.0 billion by 2034 growing at a CAGR of 24.8% during the forecast period. AI in Advanced Driver Assistance Systems (ADAS) is the integration of intelligent algorithms and machine learning techniques to enhance vehicle safety, driving efficiency, and automation. These systems analyze real-time data from sensors, cameras, and radar to detect obstacles, recognize traffic signs, monitor driver behavior, and support decision-making. AI enables features such as lane-keeping assistance, adaptive cruise control, and collision avoidance, helping reduce human error and improve overall driving experience while advancing progress toward fully autonomous vehicles.

Market Dynamics:

Driver:

Stringent vehicle safety regulations and NCAP requirements

Governments and automotive safety organizations worldwide are mandating advanced driver assistance features in new vehicles. Regulatory bodies such as the NHTSA in the U.S. and Euro NCAP have made autonomous emergency braking, lane departure warning, and pedestrian detection compulsory for high safety ratings. These regulations force automakers to integrate AI-powered ADAS into their fleets. Additionally, rising consumer awareness about road safety and insurance incentives for equipped vehicles further accelerate adoption. As safety standards become more rigorous globally, automakers are compelled to invest heavily in AI-based perception and decision algorithms. This regulatory push directly drives demand for sophisticated ADAS hardware and software, making it a primary market growth catalyst.

Restraint:

High development and validation costs of AI systems

Developing AI models for ADAS requires massive labeled datasets, high-performance computing infrastructure, and extensive real-world testing. Validation of these systems under diverse weather, lighting, and traffic conditions is time-consuming and expensive. Automakers must also comply with functional safety standards like ISO 26262, which adds complexity and cost to software development. For tier-2 and tier-3 suppliers, these upfront investments can be prohibitive, limiting market participation. Additionally, over-the-air updates and cybersecurity measures add recurring expenses. Smaller automotive manufacturers and aftermarket players often struggle to absorb these costs, slowing down widespread adoption. Consequently, high development and certification expenses remain a significant restraint in the AI in ADAS market.

Opportunity:

Rapid growth of electric and autonomous vehicles

EVs rely on efficient energy management, and AI-powered ADAS can optimize regenerative braking and route planning. Meanwhile, the development of robotaxis and Level 4 autonomous shuttles demands advanced sensor fusion and edge AI capabilities. Automakers are forming strategic partnerships with AI chipmakers and software firms to accelerate deployment. Furthermore, government funding for smart city infrastructure and autonomous vehicle testing lanes supports this growth. As consumer trust in autonomous features increases, mass-market adoption of AI-driven ADAS will expand. This convergence of electrification and automation opens new revenue streams for technology providers and automakers alike.

Threat:

Cybersecurity vulnerabilities and sensor reliability issues

AI-driven ADAS relies heavily on external sensors and connectivity, making it susceptible to cyberattacks such as sensor spoofing, GPS jamming, and adversarial AI attacks that manipulate object recognition. A compromised ADAS system could lead to false braking, steering errors, or complete system failure, endangering lives. Additionally, current sensors struggle with adverse conditions like heavy rain, fog, direct sunlight, and dirt accumulation, which degrade AI model accuracy. LiDAR and camera misalignment over time further reduces reliability. Without robust fail-safe mechanisms and real-time anomaly detection, these vulnerabilities threaten consumer acceptance. Automakers must invest heavily in redundancy, encryption, and anti-spoofing technologies. Until these threats are fully mitigated, mass adoption of high-autonomy ADAS remains at risk.

Covid-19 Impact:

The COVID-19 pandemic disrupted the AI in ADAS market through semiconductor shortages, factory shutdowns, and reduced vehicle production. Supply chain bottlenecks delayed the rollout of new ADAS-equipped models, especially for mid-range vehicles. However, the pandemic accelerated demand for contactless mobility and health-conscious driving, with features like autonomous valet parking and in-cabin air quality monitoring gaining attention. Additionally, logistics and delivery fleets adopted ADAS for safer last-mile operations. As automotive production recovers, original equipment manufacturers are prioritizing ADAS integration to meet backlogged safety regulations. The crisis also pushed automakers to localize sensor production and adopt more resilient AI development pipelines, strengthening the long-term market outlook.

The hardware segment is expected to be the largest during the forecast period

The hardware segment is expected to account for the largest market share during the forecast period. This segment includes cameras, radar sensors, LiDAR sensors, ultrasonic sensors, and electronic control units that form the physical backbone of any ADAS. The essential need for high-resolution imaging, long-range detection, and real-time processing in both entry-level and premium vehicles drives this dominance. Ongoing advancements in solid-state LiDAR and 4D imaging radar increase hardware demand.

The edge AI segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the edge AI systems segment is predicted to witness the highest growth rate. Edge AI processes data locally on vehicle chips, reducing latency and dependency on cloud connectivity, which is critical for real-time ADAS functions like automatic emergency braking. The development of specialized automotive AI accelerators, such as neural processing units, enhances on-device inference speeds while lowering power consumption. Edge AI also improves data privacy by minimizing external data transmission.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by strong presence of Tesla, General Motors, Ford, and ADAS chip suppliers like NVIDIA and Intel's Mobileye. High consumer acceptance of advanced safety features, stringent NHTSA regulations, and early adoption of semi-autonomous driving technologies fuel growth. The region also hosts major ADAS software development centers. Additionally, a mature electric vehicle ecosystem and heavy investment in autonomous ride-hailing services contribute to North America's dominant position in the global AI in ADAS market.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid vehicle electrification in China, Japan, and South Korea. Government mandates for safety technologies in India and Southeast Asia, along with aggressive localization of LiDAR and camera production, reduce system costs. Chinese automakers like BYD and NIO are integrating advanced AI models into mass-market vehicles. Expansion of autonomous mobility pilot zones and smart infrastructure projects further accelerate adoption. As fleet sizes grow and safety awareness rises, Asia Pacific becomes the fastest-growing AI in ADAS market.

Key players in the market

Some of the key players in AI in ADAS Market include Tesla, Inc., NVIDIA Corporation, Intel Corporation, Qualcomm Incorporated, Robert Bosch GmbH, Continental AG, ZF Friedrichshafen AG, Aptiv PLC, Valeo SA, Hyundai Mobis, Denso Corporation, Ambarella, Inc., Horizon Robotics, Seeing Machines Ltd., and Plus.ai.

Key Developments:

In March 2026, NVIDIA and Marvell Technology, Inc. announced a strategic partnership to connect Marvell to the NVIDIA AI factory and AI-RAN ecosystem through NVIDIA NVLink Fusion(TM), offering customers building on NVIDIA architectures greater choice and flexibility in developing next-generation infrastructure. The companies will also collaborate on silicon photonics technology.

In September 2025, NVIDIA and Intel Corporation announced a collaboration to jointly develop multiple generations of custom data center and PC products that accelerate applications and workloads across hyperscale, enterprise and consumer markets. The companies will focus on seamlessly connecting NVIDIA and Intel architectures using NVIDIA NVLink, integrating the strengths of NVIDIA's AI and accelerated computing with Intel's leading CPU technologies and x86 ecosystem to deliver cutting-edge solutions for customers.

Components Covered:

  • Hardware
  • Software
  • Services

Technologies Covered:

  • Machine Learning (ML)
  • Edge AI
  • Deep Learning
  • Sensor Fusion
  • Computer Vision
  • Natural Language Processing (NLP)

Level of Autonomy Covered:

  • L1 - Driver Assistance
  • L2 - Partial Automation
  • L3 - Conditional Automation
  • L4 - High Automation
  • L5 - Full Automation

Vehicle Types Covered:

  • Passenger Vehicles
  • Heavy Commercial Vehicles
  • Light Commercial Vehicles

Propulsion Types Covered:

  • ICE Vehicles
  • Hybrid Vehicles
  • Electric Vehicles (EVs)

Applications Covered:

  • Adaptive Cruise Control (ACC)
  • Lane Keeping Assist (LKA)
  • Surround View Systems
  • Autonomous Emergency Braking (AEB)
  • Driver Monitoring System (DMS)
  • Blind Spot Detection (BSD)
  • Traffic Sign Recognition (TSR)
  • Parking Assistance

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in ADAS Market, By Component

  • 5.1 Hardware
    • 5.1.1 Cameras
    • 5.1.2 Ultrasonic Sensors
    • 5.1.3 Radar Sensors
    • 5.1.4 Electronic Control Units (ECUs)
    • 5.1.5 LiDAR Sensors
  • 5.2 Software
    • 5.2.1 AI Middleware
    • 5.2.2 Fusion & Decision Algorithms
    • 5.2.3 Perception Software
  • 5.3 Services
    • 5.3.1 Integration & Deployment
    • 5.3.2 Training & Support

6 Global AI in ADAS Market, By Technology

  • 6.1 Machine Learning (ML)
  • 6.2 Edge AI
  • 6.3 Deep Learning
  • 6.4 Sensor Fusion
  • 6.5 Computer Vision
  • 6.6 Natural Language Processing (NLP)

7 Global AI in ADAS Market, By Level of Autonomy

  • 7.1 L1 - Driver Assistance
  • 7.2 L2 - Partial Automation
  • 7.3 L3 - Conditional Automation
  • 7.4 L4 - High Automation
  • 7.5 L5 - Full Automation

8 Global AI in ADAS Market, By Vehicle Type

  • 8.1 Passenger Vehicles
  • 8.2 Heavy Commercial Vehicles
  • 8.3 Light Commercial Vehicles

9 Global AI in ADAS Market, By Propulsion Type

  • 9.1 ICE Vehicles
  • 9.2 Hybrid Vehicles
  • 9.3 Electric Vehicles (EVs)

10 Global AI in ADAS Market, By Application

  • 10.1 Adaptive Cruise Control (ACC)
  • 10.2 Lane Keeping Assist (LKA)
  • 10.3 Surround View Systems
  • 10.4 Autonomous Emergency Braking (AEB)
  • 10.5 Driver Monitoring System (DMS)
  • 10.6 Blind Spot Detection (BSD)
  • 10.7 Traffic Sign Recognition (TSR)
  • 10.8 Parking Assistance

11 Global AI in ADAS Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 Tesla, Inc.
  • 14.2 NVIDIA Corporation
  • 14.3 Intel Corporation
  • 14.4 Qualcomm Incorporated
  • 14.5 Robert Bosch GmbH
  • 14.6 Continental AG
  • 14.7 ZF Friedrichshafen AG
  • 14.8 Aptiv PLC
  • 14.9 Valeo SA
  • 14.10 Hyundai Mobis
  • 14.11 Denso Corporation
  • 14.12 Ambarella, Inc.
  • 14.13 Horizon Robotics
  • 14.14 Seeing Machines Ltd.
  • 14.15 Plus.ai

List of Tables

  • Table 1 Global AI in ADAS Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in ADAS Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in ADAS Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global AI in ADAS Market Outlook, By Cameras (2023-2034) ($MN)
  • Table 5 Global AI in ADAS Market Outlook, By Ultrasonic Sensors (2023-2034) ($MN)
  • Table 6 Global AI in ADAS Market Outlook, By Radar Sensors (2023-2034) ($MN)
  • Table 7 Global AI in ADAS Market Outlook, By Electronic Control Units (ECUs) (2023-2034) ($MN)
  • Table 8 Global AI in ADAS Market Outlook, By LiDAR Sensors (2023-2034) ($MN)
  • Table 9 Global AI in ADAS Market Outlook, By Software (2023-2034) ($MN)
  • Table 10 Global AI in ADAS Market Outlook, By AI Middleware (2023-2034) ($MN)
  • Table 11 Global AI in ADAS Market Outlook, By Fusion & Decision Algorithms (2023-2034) ($MN)
  • Table 12 Global AI in ADAS Market Outlook, By Perception Software (2023-2034) ($MN)
  • Table 13 Global AI in ADAS Market Outlook, By Services (2023-2034) ($MN)
  • Table 14 Global AI in ADAS Market Outlook, By Integration & Deployment (2023-2034) ($MN)
  • Table 15 Global AI in ADAS Market Outlook, By Training & Support (2023-2034) ($MN)
  • Table 16 Global AI in ADAS Market Outlook, By Technology (2023-2034) ($MN)
  • Table 17 Global AI in ADAS Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 18 Global AI in ADAS Market Outlook, By Edge AI (2023-2034) ($MN)
  • Table 19 Global AI in ADAS Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 20 Global AI in ADAS Market Outlook, By Sensor Fusion (2023-2034) ($MN)
  • Table 21 Global AI in ADAS Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 22 Global AI in ADAS Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 23 Global AI in ADAS Market Outlook, By Level of Autonomy (2023-2034) ($MN)
  • Table 24 Global AI in ADAS Market Outlook, By L1 - Driver Assistance (2023-2034) ($MN)
  • Table 25 Global AI in ADAS Market Outlook, By L2 - Partial Automation (2023-2034) ($MN)
  • Table 26 Global AI in ADAS Market Outlook, By L3 - Conditional Automation (2023-2034) ($MN)
  • Table 27 Global AI in ADAS Market Outlook, By L4 - High Automation (2023-2034) ($MN)
  • Table 28 Global AI in ADAS Market Outlook, By L5 - Full Automation (2023-2034) ($MN)
  • Table 29 Global AI in ADAS Market Outlook, By Vehicle Type (2023-2034) ($MN)
  • Table 30 Global AI in ADAS Market Outlook, By Passenger Vehicles (2023-2034) ($MN)
  • Table 31 Global AI in ADAS Market Outlook, By Heavy Commercial Vehicles (2023-2034) ($MN)
  • Table 32 Global AI in ADAS Market Outlook, By Light Commercial Vehicles (2023-2034) ($MN)
  • Table 33 Global AI in ADAS Market Outlook, By Propulsion Type (2023-2034) ($MN)
  • Table 34 Global AI in ADAS Market Outlook, By ICE Vehicles (2023-2034) ($MN)
  • Table 35 Global AI in ADAS Market Outlook, By Hybrid Vehicles (2023-2034) ($MN)
  • Table 36 Global AI in ADAS Market Outlook, By Electric Vehicles (EVs) (2023-2034) ($MN)
  • Table 37 Global AI in ADAS Market Outlook, By Application (2023-2034) ($MN)
  • Table 38 Global AI in ADAS Market Outlook, By Adaptive Cruise Control (ACC) (2023-2034) ($MN)
  • Table 39 Global AI in ADAS Market Outlook, By Lane Keeping Assist (LKA) (2023-2034) ($MN)
  • Table 40 Global AI in ADAS Market Outlook, By Surround View Systems (2023-2034) ($MN)
  • Table 41 Global AI in ADAS Market Outlook, By Autonomous Emergency Braking (AEB) (2023-2034) ($MN)
  • Table 42 Global AI in ADAS Market Outlook, By Driver Monitoring System (DMS) (2023-2034) ($MN)
  • Table 43 Global AI in ADAS Market Outlook, By Blind Spot Detection (BSD) (2023-2034) ($MN)
  • Table 44 Global AI in ADAS Market Outlook, By Traffic Sign Recognition (TSR) (2023-2034) ($MN)
  • Table 45 Global AI in ADAS Market Outlook, By Parking Assistance (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.