Global Autonomous Vehicle Data Annotation Market Analysis 2020 - ResearchAndMarkets.com

DUBLIN--(BUSINESS WIRE)--The "Autonomous Vehicle Data Annotation Market Analysis" report has been added to ResearchAndMarkets.com's offering.

With the massive advancement towards the development of autonomous driving systems, no one today denies or questions the practicality of driverless vehicles. However, these vehicles have reached the deployment stage only in restricted operation design domains (ODDs). The launch of Audi's A8 (featured with level 3 functions) that reached the deployment stage in early 2019, had increased the confidence among the majority of OEMs and tier-1s; however, the level 4 and 5 vehicles still need enough time and testing to get on public roads.

This study on Autonomous Driving Data Annotation/ Labeling includes:

• An analysis of the AI and Machine learning trend and penetration rate in Automotive application.
• Analysis of the sensor data annotation for ADAS and Autonomous application - Radar, Camera, LiDAR.
• Analysis of the techniques, and tools of Data Annotation in the Deep learning models of AVs.
• Analysis of the partnership ecosystem of OEMs with technology players.
• Analysis of 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.

Key Questions Answered

• How is data annotation impacting the Autonomous and connected mobility?
• Which are the major techniques and tools for data annotation?
• How are the AV industry preferences - Manual ground truth vs automation software tools?
• Which are the major tools/ software being currently used for sensor data annotation?
• Which OEMs/ Shuttle providers are leading the race of maximum number of travelled miles? And who is following?
• Data annotation and labelling solutions: Who supplies whom?
• How is the competition between the different annotation players? Which new entrants acquiring the market share? And what challenges they are facing?
• What strategies are the annotation data providers adopting to sustain in this race?

Key Topics Covered:

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

3.9. 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

Companies Mentioned

• CMORE Automotive
• Cogito Tech
• Scale AI
• Mighty AI
• Understand.ai
• Anolytics
• Basic AI
• Avidbeam
• mCYCLOID
• Deepen.ai
• Webtunix AI
• Samasource, Inc.
• Appen
• Lionbridge Technologies, Inc.
• Awakening Vector
• Infolks Group
• Oclavi
• Dataloop

For more information about this report visit https://www.researchandmarkets.com/r/1tl1rm

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