市場調查報告書

自動駕駛車輛 (AV) 的資料註釋市場分析

Autonomous Vehicle Data Annotation Market Analysis

出版商 M14 Intelligence Research Pvt. Ltd. 商品編碼 924505
出版日期 內容資訊 英文 100 Pages
商品交期: 最快1-2個工作天內
價格
自動駕駛車輛 (AV) 的資料註釋市場分析 Autonomous Vehicle Data Annotation Market Analysis
出版日期: 2020年02月01日內容資訊: 英文 100 Pages
簡介

本報告提供自動駕駛車輛 (AV) 的資料註釋市場相關調查分析,趨勢與普及率,技術,夥伴關係生態系統,最近的M&A,種類與趨勢,分類,市場佔有率分析,主要企業的競爭評估等相關的系統性資訊。

第1章 摘要整理

第2章 ADAS、自動駕駛車輛 (AV) 的人工智能 (AI)/深層學習 (DL)的重要性

  • AV中AI技術的演進
  • AV產業的AI企業的競爭評估
  • 供應商分析

第3章 自動駕駛車資料註釋/標籤

  • 產業趨勢與未來的機會的變化
  • AV模擬,對照,檢驗的資料註釋的必要性
  • AV模擬企業製圖
  • AV資料註釋 - 最近的產業開發 (M&A,夥伴關係,JV) 製圖
  • AV資料註釋的支出或投資
  • 跟OEM/梭子供應商和資料標籤企業的第一級製圖
  • 公司內部資料註釋 vs. 來自第三方的採購
  • 資料註釋企業的競爭評估
  • 資料註釋企業的定價模式
  • ADAS感測器資料註釋

第4章 AV資料註釋:市場估計、預測

  • 資料註釋工具
  • 資料註釋技術
目錄

Description

This study on Autonomous Driving Data Annotation/ Labeling includes:

An analysis on the AI and Machine learning trend and penetration rate in Automotive application

Analysis on the sensor data annotation for ADAS and Autonomous application -Radar, Camera, LiDAR

Analysis on the techniques, and tools of Data Annotation in the Deep learning models of AVs

Analysis on the partnership ecosystem of OEMs with technology players

Analysis on the recent M&As in the annotation ecosystem and its impact on the market share of the leading players across the supply chain

Data Annotation types and trends -Manual Ground Truth and software automation

Data Annotation classification- Semantic annotation, 2D/3D cuboid bounding boxes, polyline and polygons, text and linguistic.

Market share analysis, market size in terms of revenue for a period of 2020 to 2026, pricing analysis of annotation/ labeling data along with the varying cost structure with respect to companies

Competition assessment of major players- year of experience in the industry, products/techniques, solutions offered, pricing model, funding/investment, major customers, partners, suppliers, industry ranking

Table of Contents

1. Executive Summary

2. Significance of Artificial Intelligence (AI)/ Deep Learning in ADAS and Autonomous Vehicles (AVs)

  • 2.1. AI Technology evolution in AVs
  • 2.2. Competition Assessment of AI players in AV industry
  • 2.3. Supplier analysis

3. Data Annotation/ Labeling for self-driving vehicles

  • 3.1. Changing industry dynamics and future opportunities
  • 3.2. Need for data annotation in AV simulation, verification and validation
  • 3.3. AV simulation companies mapping
  • 3.4. AV data annotation- Recent industry development (M&A, Partnerships, JVs) mapping
  • 3.5. Spending or investment on AV Data Annotation
  • 3.6. OEMs/shuttle providers and tier-1 mapping with data labeling companies
  • 3.7. In-house data annotation vs procurement from third party
  • 3.8. Competition assessment of data annotation companies
    • 3.8.1. Playment
    • 3.8.2. CMORE Automotive
    • 3.8.3. Cogito Tech
    • 3.8.4. Scale AI
    • 3.8.5. Mighty AI
    • 3.8.6. Understand.ai
    • 3.8.7. Anolytics
    • 3.8.8. Basic AI
    • 3.8.9. Avidbeam
    • 3.8.10. mCYCLOID
    • 3.8.11. Deepen.ai
    • 3.8.12. Webtunix AI
    • 3.8.13. Samasource, Inc.
    • 3.8.14. Appen
    • 3.8.15. Lionbridge Technologies, Inc.
    • 3.8.16. Awakening Vector
    • 3.8.17. Infolks Group
    • 3.8.18. Oclavi
    • 3.8.19. Dataloop
    • 3.8.20. Others
  • 3.9. Pricing models of data annotation companies- per unit annotation rate vs per hour service charges vs in-house resource acquisition for data annotation
  • 3.10. ADAS Sensor Data annotation
    • 3.10.1. LiDAR annotation
    • 3.10.2. Camera Annotation
    • 3.10.3. Radar Annotation

4. AV data annotation: Market estimation and forecast

  • 4.1. Data annotation tools
    • 4.1.1. Semantic Segmentation
    • 4.1.2. 2D/ 3D bounding boxes
    • 4.1.3. Cuboid annotation
    • 4.1.4. Landmark annotation
    • 4.1.5. Text/ Linguistic annotation
    • 4.1.6. Polygon and polyline annotation
    • 4.1.7. Audio annotation
    • 4.1.8. Video annotation
  • 4.2. Data annotation techniques
    • 4.2.1. Manual Ground-truth Labeling
    • 4.2.2. Automatic/software tools based Labeling