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市場調査レポート

自動車ヒューマンマシンインターフェース (HMI) の世界市場:音声対応バーチャルアシスタント、感情認識、ジェスチャーコントロール、および3D拡張現実

Automotive Human-Machine Interfaces: AI-Based Voice-Enabled Virtual Assistants, Emotion Recognition, Gesture Control, and 3D Augmented Reality - Global Market Analysis and Forecasts

発行 Tractica 商品コード 827281
出版日 ページ情報 英文 52 Pages; 27 Tables, Charts & Figures
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自動車ヒューマンマシンインターフェース (HMI) の世界市場:音声対応バーチャルアシスタント、感情認識、ジェスチャーコントロール、および3D拡張現実 Automotive Human-Machine Interfaces: AI-Based Voice-Enabled Virtual Assistants, Emotion Recognition, Gesture Control, and 3D Augmented Reality - Global Market Analysis and Forecasts
出版日: 2019年04月23日 ページ情報: 英文 52 Pages; 27 Tables, Charts & Figures
概要

当レポートでは、AI型自動車ヒューマンマシンインターフェース (HMI) の市場エコシステムおよび環境について、世界市場の動向、促進要因、および障壁を含めて調査しており、世界市場のセグメント別による予測、利用例およびAI型自動車HMIの関連技術の分析、主要企業のプロファイルなどを提供しています。

第1章 エグゼクティブサマリー

第2章 市場課題

  • イントロダクション
  • 自動運転車のためのコネクテッドカーについての理解
  • AI型ユーザーインターフェース技術
  • 市場動向
  • 市場成長促進要因
  • 市場障壁

第3章 AI型自動車HMIの利用例

  • コネクテッドカー
  • 自動運転車

第4章 技術課題

  • イントロダクション
  • 自然言語処理
  • マシンラーニング・ディープラーニング
  • ジェスチャー認識
  • 顔認識

第5章 主要企業

  • OEM
  • ティア1ベンダー
  • ソリューションプロバイダー

第6章 市場予測

  • 予測手法
  • トップレベルのソフトウェア収益予測
  • 自動車HMIソフトウェア収益:地域別
  • 自動車HMIソフトウェア収益:利用例別

第7章 企業ディレクトリ

第8章 頭文字・略語リスト

第9章 目次

第10章 図表一覧

第11章 分析範囲・情報源・分析手法・注記

目次
Product Code: AHMI-19

As vehicles grow increasingly connected and become packed with sensors, artificial intelligence (AI)-based technologies have the potential to play a significant role in the human-machine interface (HMI). Voice-enabled smart assistants for car controls, infotainment, and more will likely become the leading HMI in the coming era of the connected car. With momentum and regulatory demand for driver and occupant monitoring, emotion recognition and gesture recognition will become more prominent elements of HMI. Due to increasingly sophisticated computer vision algorithms, onboard compute power, and next-generation windscreen and other display technology, 3D augmented reality (AR) will become an integral component of the connected car experience.

For the connected car, AI-based HMI focuses on driver controls, driver/occupant monitoring/safety, and infotainment. For Level 5 autonomous vehicles, AI-based HMI focuses on occupant monitoring and infotainment. Most experts believe the connected car market will, with the help of aftermarket devices, grow rapidly for a number of years, then slowly decline. The fully autonomous vehicle era will likely grow more slowly due to regulatory and technical challenges. It will eventually eat away at connected car market share to a point where autonomous vehicle transportation represents the majority of the automotive market. Fast-forward several years to when humans become passengers and vehicles become moving entertainment centers in the autonomous vehicle era, and AI-based automotive HMI technologies will prove invaluable and morph into different use cases.

This Tractica report examines the market ecosystem and conditions for AI-based automotive HMI, including global market trends, drivers, and barriers. Global market forecasts, segmented by use case and region, extend through 2025. The study also explores the use cases and AI technologies related to AI-based automotive HMI and provides profiles of key industry players.

Key Questions Addressed:

  • What is the current state of the market for AI-based human-machine interfaces (HMI) and how will it develop over the next decade?
  • Which key use cases will drive greater AI-based automotive HMI adoption around the world?
  • What are the significant challenges faced by AI-based automotive HMI players?
  • Which companies are the key players in the market today and which are poised for the greatest success in the years ahead?
  • What is the size of the global market opportunity for AI-based automotive HMI?

Who Needs This Report?

  • Automotive HMI solutions providers
  • Tier 1 automotive vendors
  • Automotive OEMs
  • Voice and speech recognition software companies
  • Gesture control solutions providers
  • Emotion recognition solutions providers
  • Other AI hardware and software companies
  • Investor community

Table of Contents

1. Executive Summary

  • 1.1. Introduction
  • 1.2. Understanding the Connected Car to Autonomous Car Spectrum
  • 1.3. Market Drivers
    • 1.3.1. Population Continues to Shift into Cities
    • 1.3.2. Robo-Taxis
    • 1.3.3. China
  • 1.4. Market Barriers
    • 1.4.1. Smartphones Seen as Better Option
    • 1.4.2. Vehicle Replacement Cycle
    • 1.4.3. Hardware-Software Fusion and the Product Life Cycle Challenge
    • 1.4.4. Slow Developing Regulations for Autonomous Vehicles
  • 1.5. AI-Based Automotive HMI Use Cases
    • 1.5.1. Connected Car
      • 1.5.1.1. Driver, Occupant Controls
      • 1.5.1.2. Driver, Occupant Monitoring
      • 1.5.1.3. Infotainment
    • 1.5.2. Autonomous Car
      • 1.5.2.1. Occupant Monitoring
      • 1.5.2.2. Infotainment
  • 1.6. Market Forecasts
  • 1.7. Conclusions and Recommendations

2. Market Issues

  • 2.1. Introduction
  • 2.2. Understanding the Connected Car to Autonomous Car Spectrum
  • 2.3. AI-Based User Interface Technologies
    • 2.3.1. Voice/Speech Recognition
    • 2.3.2. Gesture Recognition
    • 2.3.3. Emotion Recognition
  • 2.4. Market Trends
    • 2.4.1. Disrupted Market Ecosystem
    • 2.4.2. Hyperscaler Motivation
  • 2.5. Market Drivers
    • 2.5.1. Population Continues to Shift into Cities
    • 2.5.2. Robo-Taxis
    • 2.5.3. China
    • 2.5.4. Reduce Distracted Driving Accidents
    • 2.5.5. Shifting Consumer Desire for Privately Owned Vehicles
    • 2.5.6. Carmakers Shifting to Technology and Mobility Providers
    • 2.5.7. 5G Connected Cars
  • 2.6. Market Barriers
    • 2.6.1. Smartphones Seen as Better Option
    • 2.6.2. Vehicle Replacement Cycle
    • 2.6.3. Hardware-Software Fusion and the Product Life Cycle Challenge
    • 2.6.4. Slow Developing Regulations for Autonomous Vehicles

3. AI-Based Automotive HMI Use Cases

  • 3.1. Connected Car
    • 3.1.1. Driver, Occupant Controls
    • 3.1.2. Driver, Occupant Monitoring
    • 3.1.3. Infotainment
  • 3.2. Autonomous Car
    • 3.2.1. Occupant Monitoring
    • 3.2.2. Infotainment

4. Technology Issues

  • 4.1. Introduction
  • 4.2. Natural Language Processing
    • 4.2.1. The Importance of Machine and Deep Learning to NLP
    • 4.2.2. Understanding Natural Language: Word Maps and Language Models
    • 4.2.3. Natural Language Generation
    • 4.2.4. Legacy Approaches to NLP
      • 4.2.4.1. Rules-Based
      • 4.2.4.2. Statistical Models
    • 4.2.5. Speech Recognition
    • 4.2.6. Voice Recognition
  • 4.3. Machine and Deep Learning
    • 4.3.1. What Is Deep Learning?
  • 4.4. Gesture Recognition
    • 4.4.1. Challenges and Limitations
    • 4.4.2. Gesture Types
      • 4.4.2.1. Body Language
      • 4.4.2.2. Eye Motion
    • 4.4.3. Semaphoric Commands
    • 4.4.4. Response and Disambiguation
    • 4.4.5. 3D Skeletal Tracking and Pose Estimation
    • 4.4.6. Gaze Tracking
    • 4.4.7. Space-Aware Interfaces
      • 4.4.7.1. Depth-Aware Cameras
      • 4.4.7.2. Stereo Cameras
  • 4.5. Facial Recognition
    • 4.5.1. Real-Time Video Analytics

5. Key Industry Players

  • 5.1. OEMs
    • 5.1.1. BMW
    • 5.1.2. BYTON
    • 5.1.3. Honda
    • 5.1.4. Toyota
  • 5.2. Tier 1 Vendors
    • 5.2.1. Harman
    • 5.2.2. Magneti Marelli
    • 5.2.3. Mitsubishi Electric Automotive America
  • 5.3. Solutions Providers
    • 5.3.1. Alibaba
    • 5.3.2. Amazon
    • 5.3.3. Apple
    • 5.3.4. Baidu
    • 5.3.5. Eyeris
    • 5.3.6. eyeSight Technologies
    • 5.3.7. Google
    • 5.3.8. iFlytek
    • 5.3.9. Nuance
    • 5.3.10. SiriusXM
    • 5.3.11. SoundHound
    • 5.3.12. WayRay

6. Market Forecasts

  • 6.1. Forecast Methodology
  • 6.2. Top Level Software Revenue Forecast
  • 6.3. Automotive HMI Software Revenue by Region
  • 6.4. Automotive HMI Software Revenue by Use Case
    • 6.4.1. Voice and Speech Recognition
    • 6.4.2. Driver Face Analytics and Emotion Recognition
    • 6.4.3. Gesture Recognition

7. Company Directory

8. Acronym and Abbreviation List

9. Table of Contents

10. Table of Charts and Figures

11. Scope of Study, Sources and Methodology, Notes

Tables

  • Automotive HMI Software Revenue, World Markets: 2018-2025
  • Automotive HMI Software Revenue by Use Case, World Markets: 2018-2025
  • Automotive HMI Software Revenue by Region, World Markets: 2018-2025
  • Automotive HMI Software Revenue for Voice and Speech Recognition by Region, World Markets: 2018-2025
  • Automotive HMI Software Revenue for Driver Face Analytics and Emotion Recognition by Region, World Markets: 2018-2025
  • Automotive HMI Software Revenue for Gesture Recognition by Region, World Markets: 2018-2025

Figures

  • Volvo's 360c Autonomous Concept Car, September 2018
  • Robo-Taxi Vehicle Tested in London
  • Fleet Cars in China
  • Yanfeng Automotive Interiors Concept
  • Examples of Word Embeddings
  • Progression of Natural Language Generation
  • Artificial Intelligence Encompasses Numerous Technologies
  • Schematic Representation of a Deep Neural Network
  • BYTON SUV Control Console
  • Harman's Ignite Platform
  • Magneti Marelli's Maiya
  • Amazon Echo Auto
  • Baidu's High Definition Maps
  • eyeSight Driver Identification
  • WayRay Holographic AR

Charts

  • Automotive HMI Software Revenue, World Markets: 2018-2025
  • Automotive HMI Software Revenue by Use Case, World Markets: 2018-2025
  • Automotive HMI Software Revenue by Region, World Markets: 2018-2025
  • Automotive HMI Software Revenue for Voice and Speech Recognition by Region, World Markets: 2018-2025
  • Automotive HMI Software Revenue for Driver Face Analytics and Emotion Recognition by Region, World Markets: 2018-2025
  • Automotive HMI Software Revenue for Gesture Recognition by Region, World Markets: 2018-2025
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