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Introduction
Data annotation is accelerating intelligent systems by enriching AI training datasets for improved decision-making. From autonomous vehicles to virtual assistants, smarter annotations boost accuracy and safety. Achieving full autonomy in vehicles requires millions of test miles, which industries address through automated workflows, real-world, and virtual simulations. However, these still rely on large volumes of high-quality annotated data.
With the autonomous navigation market expected to reach $13.5 billion by 2030, intelligent annotation is a key enabler—driving faster development, enhanced scenario modeling, and real-time decision-making. As data becomes the new fuel, smart annotations are vital to scaling the digital revolution efficiently and safely.
Enhancing Vehicle Recognition with AI-Powered LiDAR Object Detection
Business Challenge
The race to autonomy demands massive volumes of accurately annotated data, but manual annotation is slow, costly, and inconsistent. As industries accelerate digital transformation, the lack of scalable, intelligent annotation workflows creates a bottleneck—hindering development speed, data quality, and system performance. Bridging this gap is critical to achieving reliable, real-time decision-making in intelligent and autonomous systems.
Here's How We Help
Fast & Efficient Data Annotations for precise data with precise quality and quantity is the need of the hour. Most autonomous and intelligent systems require visual perception systems for recognizing the content of images/scenes. Furthermore, this is an important factor for automatized vehicles/systems to take decisions.
Any automated system has to deal with four major questions:
- Understanding the environment?
- Communicating with the environment?
- How to respond?
- Why people behave the way they do?
Unless a system can handle at least 3 of these questions, it is difficult for the system to behave independently, intelligently, and earn human trust.
Much of the testing has to be done for “training” the systems to recognize various thorny situations which require precise data at a precise quality and the precise data quantity. Smart data generation, and augmentation based on machine learning is the evolving method to get the precise data with relevant quality and quantity. Data annotations take up around 70% of training and development time for the algorithms. This solution ensures faster development and safer systems with operational efficiency.
Service Framework
Key Features
Core Data Annotation & Labeling Capabilities
- Automatic labeling for data generation
- Selective frame tagging & corrections
- Hierarchical image tagging and semantic segmentation
- Named entity recognition- Action detection, Scene recognition, Face recognition, Sentiment analysis
- Interpolation – Automatic object tagging throughout all frames where object occurs
- Point Tracker approach for tag and object correlation
AI-Driven Data Enhancement & Model Ecosystem
- Data augmentation using AI/ML algorithm for validation data creation
- AI model repository for different applications
Deployment Flexibility & System Extensibility
- Cloud / On-premise deployment
- Easy plug-in of third-party modules
- Flexible to scale and accommodate new requirements
Why Tata Elxsi?
- Customized AI models for recognizing the content of images/scene for automatized vehicles/driving scenarios
- Fast & Efficient Data Annotations
- Flexible data generation workflow with customizability for manual, assisted, partially automatic, and complete automated labeling
- Mange complete workflow from one centralized application
- Interpolation – Automatic object tagging throughout all frames where object occurs
- Self-learning AI engine for historic corrections/annotations
Customer Testimonial
Customer Testimonial
A Japan-based ADAS & Autonomous Systems Supplier
"The automotive industry is entering into the era of Vehicle intelligence marked by the rise of ADAS and Autonomous functionalities. The complex SW logic driving this technology requires rigorous vehicle testing and virtual validation setups. We have collaborated with Tata Elxsi Limited for developing a tool that can generate a plethora of virtual parking scenarios from a handful of base scenes in an automated fashion. The use of this tool can be extended to Autonomous driving scenario generation also with minimal changes. We have also developed a "Sonar ECU recognition Logic" that can sense Obstacles in virtual environment and send that information to Autonomous ECU for further processing. We acknowledge Tata Elxsi's efforts and thank them for supporting us in transforming our concept into reality."
