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市場調查報告書

自動駕駛技術開發中AI的影響

Impact of Artificial Intelligence on Autonomous Driving Development

出版商 Frost & Sullivan 商品編碼 593843
出版日期 內容資訊 英文 51 Pages
商品交期: 最快1-2個工作天內
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自動駕駛技術開發中AI的影響 Impact of Artificial Intelligence on Autonomous Driving Development
出版日期: 2017年11月21日 內容資訊: 英文 51 Pages
簡介

自動駕駛技術產業的競爭以爆炸性的速度前進之中,駕駛的各個方面迎接改革和變革,帶來該變革的,是持有想像以上的能力,自動駕駛的人工智慧的開發。

本報告提供人工智能帶給自動駕駛技術產業的影響的分析等。

第1章 摘要整理

  • 主要調查結果
  • 自動駕駛中AI的開發活性主要的因素
  • 與AI有關係的自動化的等級
  • 自動駕駛中AI的全球擴大 - 重要之柱
  • 自動駕駛中AI開發的價值鏈
  • AI支援的關注的企業 - 各地區
  • 主要的技術公司方法 - 概要
  • 自動駕駛中AI的鄰接商機
  • 自動駕駛中AI實行主要課題
  • 主要趨勢

第2章 調查的範圍與分類

  • 調查範圍
  • 本報告明白的事

第3章 自動駕駛AI vs. 傳統方法

  • 傳統方法 vs. 深度學習方法
  • AI - 主要差別化因素
  • AI開發對軟體的依賴
  • 自動駕駛中AI的進展
  • 開發AI的汽車產業的破壞
  • 自動駕駛汽車的AI的資料流程所扮演的角色

第4章 AI的深度學習

  • 活化AI的自我學習的DNN
  • 深度神經網 - 訓練循環
  • 自動駕駛用深度學習引進的課題
  • 機器學習方法 - 案例研究1:Oxbotica
  • 深度學習方法 - 案例研究2:Drive.ai
  • CNN - 案例研究3:AIMotive

第5章 聯盟的創新

  • NVIDIA - 完全的終端間AI解決方案:硬體設備
  • NVIDIA - 完全的終端間AI解決方案:DL軟體
  • NVIDIA的活動 - 關注的聯盟
  • 先進企業 - 概要

第6章 主要OEM的活動

  • 主要OEM與AI - 互相評估

第7章 成長機會與應行動企業

  • 成長機會 - 來自OEM/TSP的投資和合作
  • 成功和成長的策略性必要事項

第8章 結論與今後展望

  • 結論與今後展望
  • 法律上的免責聲明

第9章 關於Frost&Sullivan

目錄
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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