Data Annotation Services
for Automotive OEMs and Their Suppliers
Powering autonomous vehicles with high quality training data and accelerating AI development processes
Make Sure You Have the Highest Quality Dataset for Your Project
Although implementing AI in a factory or service serving the automobile sector may incur some initial costs, in the long run, you get a lot of value for the money you spend. AI enables industry players, including automakers, to achieve previously unimaginable levels of efficiency and technology. This covers autonomous vehicles, ADAS, AI-powered factory floor robots, proactive maintenance, and more. AI enables autonomous vehicles and driving technology development from L1 to L5, providing drivers with information about their surroundings in real time. In the industry, there are many different use cases for AI, each with unique advantages.
This includes things like ADAS technologies that increase overall safety of drivers on the road, AI entertainment inside vehicles and, of course, helping fully autonomous vehicles become mainstream. So far, the vision for the automotive industry has far exceeded the pace of its progress, but with better quality datasets, all the futuristic technologies we dreamed about could become a reality.
World-class niche expertise in Automotive
Image & Video Segmentation for HD Mapping
Image and video segmentation are some of the fundamental steps toward scene understanding of machines. Thus, HD maps become more accurate and less costly to produce.
Object and Event Recognition for ADAS
Recognizing both the moving and static objects is important in order to improve the accuracy of ADAS technology. The higher the accuracy, the higher level of driving safety they offer.
Road Scene Recognition & Analysis for Collision Prevention
Understanding the context of the scene is one of the most important aspects for the new generation of autonomous vehicles. This will help prevent accidents and help with the adoption of self-driving cars.
In-Cabin Behavior Recognition & Annotation
Modern driver monitoring systems evaluate driver behavior by means of distinctive sensor technology and, if necessary, indicate undesirable driving behavior. This technology needs to be implemented in as many vehicles as possible.
LiDAR annotation is essential to the success of advanced autonomous cars and ML models that must comprehend multi-component urban and natural surroundings since it transforms complex 3D data into training datasets.
Identifies objects in a 3D point cloud and draws bounding cuboids around the specified objects, returning the positions and sizes of these boxes. Instead of using light to measure distances, it uses radio waves.
Geospatial Data Annotation
Help your geospatial models employing computer vision, artificial intelligence, and machine learning, to better navigate the road and extract real-world objects and events from geographical or aerial imagery. Enhance the performance and precision of your geospatial machine learning model with accurately annotated and labeled images.
Speech Recognition & Sound Labeling for Voice & Security Assistants
Get all the information you need with next-generation voice interaction capabilities with almost instant voice wake-up, advanced AI-based speech recognition and dedicated audio systems that help digital personal assistants react faster and smarter than ever before.
Multi - Sensor Fusion
This is a technology that enables researchers to combine information from several sources in order to form a unified picture of any given environment. Data from computer vision cameras, LiDAR, Radar and other sources can be combined to overcome the challenges of each individual sensor.
Artificial intelligence (AI) will make autonomous vehicles more commonplace over the course of the next few decades. Simultaneously, AI will revolutionize the majority of auto production processes, from research and design to project management and administrative tasks.
Build Me a Team
We have a minimum threshold for starting any new project, which is 735 productive man-hours a month (equivalent to 5 graphic annotators working on the task monthly).