ADAS and AV Data Annotation Applications Market, Edition 2020
ADAS and AV development, testing, verification, and validation with Image, Video, Data Annotation, Ground Truth Labelling, Automation Software and Manual, Annotation Tools, Emerging Trends, Market Forecast, and Suppliers Analysis
In this study, you will find detailed analysis on every aspect of future demand for data annotation in ADAS and AV industry. Some of the critical analysis covered in the report are as follows:
An analysis on the annotation trend and penetration rate in ADAS and AV application
Analysis on annotation types for automotive application – image annotation, labelling, video annotation, and data annotation
Analysis on the techniques, tools, requirements of annotation, and types of services offered by the annotation players
Analysis on the partnership ecosystem of AV Testing &Verification companies with technology players and data labelling providers
Analysis on the recent M&As in the ecosystem and its impact on the market share of the leading players across the supply chain
Annotation analysis based on type of services – Ground truth labelling, annotation software and manual labelling of data
Annotation analysis based on tools–2D boxes/bounding, semantic segmentation, polygons/contour, 3D cuboids, 3D point cloud labelling
With this research, we aim to bring a fact-based evaluation of the AVs. As consultants, we also look forward to help you create your next go-to market strategy to position yourself as a key player in this swiftly evolving AV landscape.
This report is an outcome of M14 INTELLIGENCE leverage over the exhaustive research database on the ACES industry and its enabling technologies. Total 4 years of collective desk research and interviews by a team of 5 full-time ACES consultants
This report is the industry’s 1st successful annual publication on the ADAS and AV Data Annotation Market. Over 200 pages on the ADAS and AV data annotation techniques, market landscape, and supplier’s analysis. With 24 interviews conducted with the data annotation companies in 2019-20 and over 1500 interviews with key stakeholders within the AV industry till date since 2016
This report covers —
An analysis on the AI and machine learning trend and penetration rate in autonomous driving application
Analysis on the sensor data annotation for ADAS and autonomous driving application – radar, camera, and 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.
Testing and Validation Market Size analysis with penetration of different testing platforms including cloud-based, AI-based, simulator based, and PC based
Market share analysis, market size in terms of revenue for a period of 2020 to 2030, 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 pointers of interest are –
AUTONOMOUS VEHICLE DEMAND ANALYSIS
Historic quarterly and yearly ADAS And AV sales analysis
Estimation and forecast of ADAS & AV sales till 2030
ADAS & AV market by level of automation
Penetration of AI and machine learning
AV ANNOTATION MARKET BY TYPE OF SOURCES
Image annotation and Labelling
LiDAR data annotation
AV ANNOTATION MARKET SEGMENTATION
Market penetration rate of annotation and labelling in AV industry breakdown by –
Tools – 2D Boxes/Bounding, Semantic Segmentation, Polygons/contours, 3D Cuboids, 3D point cloud labelling, key points, line
Types of services- Ground Truth, Automation Software, manual annotation
AV ANNOTATION MARKET FORECAST
AV Annotation Market Revenue ($ million), 2019 to 2030
AV Annotation Pricing Analysis
Parent market- ADAS and Autonomous passenger and commercial vehicle market analysis
AV Annotation customer analysis –
Autonomous Shuttle providers and robotaxi companies
AI and software providers
FUTURE TRENDS MAPPING
Future technological innovations in the AV annotation market
Ground Truth Vs Software based
Competition between annotation and data labelling players
Competition between AI and software players in the AV industry
Company profiles and their future plans
INORGANIC DEVELOPMENT STRATEGIES
Partnership mapping within the ecosystem – OEMs, AI and software service provider, data labelling and annotation players
M&As, Collaborations, Agreements, Funding, and Investments
Cloud-based testing platform have higher growth potential compared to other platforms with estimated penetration rate of XX percent in 2020
Artificial intelligence has penetrated AV testing market with a faster pace; however, AI would take some time to attain maturity. Around XX percent share is expected to be acquired by AI based AV testing by 2025
By 2030, the rate of penetration of simulators for AV testing is expected to grow to XX percent, from XX percent in 2020
Perception is the basis for a vehicle to be able to drive itself (without a driver). The vehicle with high automation should be trained enough to track, classify, differentiate the objects in the vicinity in order to decide on its course of action.
Moreover, envisaging the path of moving entities is determined as the next most important ability to be acquired by highly automated vehicles.
This could be attained by rigorous testing and validation under enormous datasets including multiple scenarios. Data annotation or labeling of objects plays a vital role in this by automating and fast tracking the process.
Annotation is the process of labeling the object of interest in the image or video by using bounding boxes to help AI or Machine Learning models understand and recognize the objects detected by sensors.
In the ADAS development process, high volume of data is acquired from the test fleet through the cameras, ultrasonic sensors, radar, LIDAR, and GPS, which is then ingested from vehicle to the data lake.
This ingested data is labeled and processed to build a testing suite for simulation, validation and verification of ADAS models. In order to get autonomous vehicles quickly on public roads, huge training data is required, and the current shortage of it, is the biggest challenge.
Huge amount of rich and diverse labelled data is the most precious asset require for training and validation of autonomous vehicles. Ground truth annotation involves collection of the information on location, allowing the image data to relate to the reality on ground.
This annotated data assist in training and validating the perception and prediction models with precision. For autonomous vehicle, ground truth labeling helps in annotating urban scenarios, highway environments, road markings and sign boards, and different weather conditions that enables to efficiently train and detect moving objects.
Manual labeling of this huge dataset requires significant resources, time and money. Several automation software tools and labelling apps that have evolved recently provides frameworks to create algorithms to automate the labeling process, ensuring the same precision and safety.
Some of the open source automatic annotation tools include Amazon SageMaker Ground Truth, MathWorks Ground Truth Labeler app, Intel’s Computer Vision Annotation Tool (CVAT), Microsoft’s Visual Object Tagging Tools (VoTT), DataTurks, LabelMe, Fast Image Data Annotation Tool (FIAT), COCO Annotator, Scalabel by DeepDrive, RectLabel, and Cloud-LSVA.
The autonomous driving race between different players in the ecosystem is becoming aggressive to showcase the most precise and fluent system capable of operating in any weather conditions.
Majority of the players are adopting Artificial Intelligence (AI) and Machine Learning (ML) to train their AVs. Huge data captured from sensors needs to be labeled or annotate to accurately train these machine learning models.
This market holds billion-dollar business potential behind the actual AV industry. Majority of automotive OEMs/ Tier-1s have started outsourcing the data labelling, while few of them find it painful paying third parties and hence preferred in-house data annotation.
For example, Waymo with highest number of autonomous test miles travelled, have in-house annotation dataset of approximately 25 million 3D bounding boxes and 22 million 2D bounding boxes. Also, Tesla has 1.3 million miles of data gathered from its Autopilot equipped vehicles.
As companies are stepping towards the production stage of AVs, the data annotation requirement is scaling up exponentially. It becomes challenging for the companies to internally meet this mounting demand of training datasets and hence the companies are moving towards outsourcing of annotation data.
Specialized annotation companies serving in the self-driving industry includes CMORE Automotive, Understand.ai, and FEV Group from Germany; United States based Cogito Tech, Scale AI, Anolytics, Basic AI, Deepen.ai, Samasource, Inc., Appen, Lionbridge Technologies Inc.; Playment, mCYCLOID, GTS Ltd, Infolks Group, and Oclavi are few of the well-known companies headquartered in India.
There has been a massive development in the data labeling industry from past two to three years in India. Several start-ups have emerged in this region making it a hub for ML datasets with quality and innovative solution offerings.
CMORE Automotive, a well-known German software tools and measurement systems provider has formed a joint venture with Expert Global Solutions (EGS) based in Aurangabad, India to form ‘EC. Mobility’ which is focused on autonomous driving data annotation.
Other companies with high growth potential in this field includes Egypt based Avidbeam, Israeli Dataloop, and Canada’s Awakening Vector. Amongst these Avidbeam has comparatively more years of experience with 30+ experts or engineers working on the annotation database serving industries such as smart cities, retail, automotive, industrial and consumer space.
Key Questions Answered
How is data annotation impacting the autonomous and connected mobility?
Which are the major techniques and tools for data annotation?
Where is the market concentrated (in terms of value) -Manual Ground-truth Labeling or Automation/software?
Which are the major tools/software being currently used for sensor data annotation?
Which OEMs/robotic vehicles providers are leading the race of maximum number of traveled miles? And who are 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 are they facing?
What strategies the annotation data provided adopting to sustain in this race?
What are the factors pushing the need for data annotation in autonomous vehicle industry?
What challenges are the stakeholders in this industry facing?
Market estimation and forecast for data annotation in AV industry?
List of Companies
*not an exhaustive list, please download the sample to see more info
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