Table of Content

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

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