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自動運転技術開発におけるAIの影響

Impact of Artificial Intelligence on Autonomous Driving Development

出版日: | 発行: Frost & Sullivan | ページ情報: 英文 51 Pages | 納期: 即日から翌営業日

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自動運転技術開発におけるAIの影響
出版日: 2017年11月21日
発行: Frost & Sullivan
ページ情報: 英文 51 Pages
納期: 即日から翌営業日
  • 全表示
  • 概要
  • 目次
概要

自動運転技術業界の競争が爆発的な速度で進むなか、運転のあらゆる側面が改革と変革を迎え、その変革をもたらしているのが、想像しうる以上の能力をもつ、自動運転における人工知能(AI)の開発です。

当レポートは、人工知能が自動運転技術業界に与える影響の分析などについて取り上げています。

第1章 エグゼクティブサマリー

  • 主な調査結果
  • 自動運転におけるAIの開発活性の主な因子
  • AIが関係するとされる自動化のレベル
  • 自動運転におけるAIの世界拡大 - 重要な柱
  • 自動運転におけるAI開発のバリューチェーン
  • AI対応の注目の企業 - 地域別
  • 主な技術会社のアプローチ - 概要
  • 自動運転におけるAIの隣接収益機会
  • 自動運転におけるAI実装の主な課題
  • 主な動向

第2章 調査の範囲と分類

  • 調査範囲
  • 当レポートでわかること

第3章 自動運転AI vs. 従来型アプローチ

  • 従来型アプローチ vs. ディープラーニングアプローチ
  • AI - 主な差別化因子
  • AI開発のソフトウェアへの依存
  • 自動運転におけるAIの進捗
  • AIを開発する自動車業界の破壊
  • 自動運転自動車におけるAIのデータフローの役割

第4章 AIにおけるディープラーニング

  • AIの自己学習を活性化するDNN
  • ディープニューラルネットワーク - トレーニングサイクル
  • 自動運転のためのディープラーニング導入の課題
  • 機械学習アプローチ - ケーススタディ1:Oxbotica
  • ディープラーニングアプローチ - ケーススタディ2:Drive.ai
  • CNN - ケーススタディ3:AIMotive

第5章 提携による革新

  • NVIDIA - 完全な終端間AIソリューション:ハードウェア
  • NVIDIA - 完全な終端間AIソリューション:DLソフトウェア
  • NVIDIAの活動 - 注目の提携
  • 先進企業 - 概要

第6章 主なOEMの活動

  • 主要OEMとAI - 相互評価

第7章 成長機会と行動すべき企業

  • 成長機会 - OEM/TSPからの投資と提携
  • 成功と成長のための戦略的必須事項

第8章 結論と今後の展望

  • 結論と今後の展望
  • 法的免責事項

第9章 Frost & Sullivanについて

目次
Product Code: K1B1-18

6 OEMs to Have Ai-incorporated Autonomous Driving Software by 2022 but to be Focused on Object and Road Furniture Detection Rather than on Core Decision Engine Software

With the autonomous vehicle industry racing from zero to warp speed, every aspect of the driving world is set for innovation and transformation, and Artificial Intelligence (AI) development in autonomous driving is to bring that transformation, as it is capable of achieving more than what can be imagined. For situations that require hours of programming for dealing with one particular scenario while driving can now be dealt by a deep neural network, wherein the data scientist just needs to expose the DNN to thousands of images from which it can learn. For true enablement of Level 4 and Level 5 automated driving, the system should be functional in all weather and driving conditions. Deep learning is expected to be the most adopted approach to develop AI as it learns and starts to think by itself without the need of regular human intervention. This means that the AI will be capable of dealing with the several use cases displaying advanced levels of thinking which is required for autonomous vehicle to function in the real world. This is what is happening in AI development for robotics, which is briskly percolating for AD development. Using deep neural networks, the system can make decisions that provide a clear understanding of the driving scenarios and can make justified decisions when driving in the autonomous mode. Besides safety and autonomous driving, AI would be present in several aspects in the automotive industry such as speech recognition, computer vision, connected cars, and virtual assistants. OEMs in the market would like to partner with skilled startups to develop their capabilities to a broader sense. Advantages of using the AI approach include low lead time for development, ease of testing, addition of a wider range of use cases for autonomous driving, and reduced cost of development as compared to the traditional approach. Object detection, classification, and subsequent learning for decision making based on an internally learnt algorithm to help fasten development. The industry still remains uncertain of the actual power of AI. Direct access to cars enables hackers to compromise the security of the vehicle and user. Data ownership and usage rights are another key concern for end users. Currently, all data gathered are owned by the OEMs. It is difficult for the programmers to validate what the system has learnt after training. Several simulations are required to assess the software capability. Moreover, the industry today lacks a well-defined framework for use of AI in autonomous driving.

Table of Contents

1. EXECUTIVE SUMMARY

  • Key Findings
  • Top Trends Driving the Development of AI for AD
  • Levels of Automation Defined With Regard to AI
  • Expanding Universe of AI in AD-Vital Pillars
  • Value Chain Development of AI in Universe of AD
  • Noteworthy Companies With AI Capabilities-By Region
  • Major Tech Companies' Approach-Overview
  • Adjoining Revenue Opportunities for Artificial Intelligence in AD
  • Major Challenges in Implementation of AI in AD
  • Key Trends

2. RESEARCH SCOPE AND SEGMENTATION

  • Research Scope
  • Key Questions This Study will Answer

3. AUTOMATED DRIVING ARTIFICIAL INTELLIGENCE VERSUS TRADITIONAL APPROACH

  • Traditional Approach Versus Deep Learning Approach
  • AI-Key Differentiators
  • Dependence of AI Development on Software
  • Progression of AI in Autonomous Vehicles
  • Disruption in the Automotive Industry with Developing AI
  • Role of Data Flow in AI in AD Cars

4. DEEP LEARNING IN AI

  • DNN to Drive Self-learning AI
  • Deep Neural Network-Training Cycle
  • Challenges for Deep Learning Adoption for AD
  • Machine Learning Approach-Case Study: Oxbotica
  • Deep Learning Approach-Case Study 1: Drive.ai
  • CNN-Case Study: AIMotive

5. INNOVATION THROUGH PARTNERSHIPS

  • NVIDIA-A Complete End-to-end AI Solution: Hardware
  • NVIDIA-A Complete End-to-end AI solution: DL Software
  • NVIDIA'S Activity-Highlighted Partnerships
  • Companies Ahead in the Business-Overview

6. MAJOR OEM ACTIVITIES

  • Major OEMs and AI-How They Rate Against Each Other?

7. GROWTH OPPORTUNITIES AND COMPANIES TO ACTION

  • Growth Opportunity-Investments and Partnerships from OEMs/TSPs
  • Strategic Imperatives for Success and Growth

8. CONCLUSIONS AND FUTURE OUTLOOK

  • Conclusion and Future Outlook
  • Legal Disclaimer

9. THE FROST & SULLIVAN STORY

  • The Frost & Sullivan Story
  • Value Proposition-Future of Your Company & Career
  • Global Perspective
  • Industry Convergence
  • 360° Research Perspective
  • Implementation Excellence
  • Our Blue Ocean Strategy
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