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Understand the
road ahead provides high-quality training and validation data to enable mobility companies to develop with confidence computer vision and machine learning models that reliably and safely power autonomous vehicles.

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Ground Truth quality annotations

Ground Truth quality

Achieve the most reliable safety standard and the best results by training and validating your algorithms with accurate annotations made with German precision.

Fast and flexible scalability

Fast and flexible

Our system is fast and flexible in its approach. Manage projects with more than 100 million annotated images up to 5 times faster than conventional approaches.

Production-grade Annotation Toolchain

Annotation Toolchain

Benefit from our track-proven SaaS tool chain to enable your large scale annotation and validation projects. Our tools and features are optimized to handle large complexity. Some of the most prestigious self driving projects rely on our tooling.

Real-world Scenario Generation

Real-world Scenario

Challenge your driving functions with edge-case scenarios derived from real-world recordings directly in your simulation. We help you achieve a more complete coverage of the enormous test space through meaningful variations of the base scenarios.

Known from
  • deutsche startups
  • techtag
  • Wirtschaftswoche Gründer
  • VentureCaptial Magazin
  • The Irish Times
  • NKF

Training and Validation Data

Our portfolio covers the broad diversity of all regular raw data formats, project requirements, annotation and scenario types. This includes 2D images, semantic enrichments, video annotations as well as labeling of LiDAR and RADAR point clouds. This is of course only a simplified summary. If your specifications are not listed below, we most likely still support them.

3D Boxes in Point Cloud
Raw Point Cloud

3D Boxes in LiDAR
Point Clouds

LiDAR is an active optical sensor technology which scans the earth’s surface to determine highly accurate x, y and z measurements. It transmits laser beams to a specific object and reflects its movements back to the receiver and analyzes the time span and distance with GPS and INS information to construct a 3D point cloud of reflective obstacles. LiDAR adds complementary information and is very strong at detecting pedestrians and other non-metallic objects at night, a scenario in which camera and RADAR sensors often fail.

Within this valuable raw data, we are able to identify objects in a 3D point cloud by annotating 3D bounding boxes or cuboids around the specified object.

Semantic Segmentation
Raw measured data

Pixelwise Segmentations

Since the world is not made out of boxes, we are also offering a more precise method to annotate your data - semantic segmentation.

Depending on the raw data, bounding boxes can contain noise in the form of background and occlusions. This is tackled with semantic segmentation, where each pixel assigned to the class of your selected objects will be annotated. It is therefore the closest to a true representation of reality in 2D space, regarding class assignments. It also is more versatile, since it is very easy to distinguish between objects, e.g., road, lanes and curbs and track instances of them throughout the sequence. It is also possible to annotate classes that are not instantiable, e.g. groups of pedestrians. Pixelwise Semantic Segmentation is often used for training the neural networks on annotated images or videos.

Bounding Box

Bounding Boxes

The annotation type with the most scientific research and most commonly used is bounding boxes. They are easy to apply to machine learning models and faster to annotate in comparison to other annotation types.

Unlike segmentation, bounding boxes may also contain invisible parts of the classified object by approximating occlusions or truncations. Due to the inherent instance-awareness of bounding boxes, your algorithms will get a better understanding of the concept of specific objects and -if you want- track certain objects throughout the sequence. Bounding Boxes are most often used for testing and validation of new sensors or for tracking of objects in sequential data.

Scenario Generation

Since the test space for autonomous driving functions is incredibly large, it is not feasible to validate new automated driving functions with real-world driving tests. We bring the complexity of reality into your simulation via highly accurate, complex simulation scenarios and their variations derived from real-world sensor measurements.

This helps challenging driving functions in a very realistic way in the simulation environment early in the development process. This enables frontloading which incorporates finding and eliminating bugs as early as possible, thus saving a lot of time and money.


Identity Protection Anonymizer

Anonymization of faces and license plates has become a global requirement. With new regulations from the European GDPR, Californian CCPA, Chinese CSL and Japanese APPI, autonomous vehicles need to be able to collect street scene data globally with all the critical personal information automatically removed.

Our AI-powered anonymizer tool, called Identity Protection Anonymizer, ensures your data is compliant by blurring faces and number plates in a fully automated fashion. Our system can be used on premise or as a service via the Cloud Platform. In both cases we make sure the anonymizer provides the highest quality data at the scale required.

Why the industry chooses us

We are proud to work with the most innovative and technologically advanced mobility companies and suppliers. Let us introduce our team of experts to your next project.

Dr. Florian Faion Research Scientist LiDAR Perception - BOSCH

Highly accurate annotation is an indispensable prerequisite for supervised machine learning. We rely on the labeling service and tools from

Peter Schlicht Project Manager „AI-Technologies for automated driving – VW Research Group is not only software, you also get a dedicated team that is knowledgeable and confident to exchange competencies and creative solution-oriented approaches, which definitely enriched our work. We are fully satisfied with the results of the cooperation, especially the model structures of the AI applications and the interpretation of the results exceeded our expectations. Thanks to we are now much further in our understanding of the necessity of high-quality data and attained a clearer picture of how to achieve a secure AI application for automated driving.

Veronika Cheplygina Ph.D. Assistant Professor
Eindhoven University of Technology responded very quickly and were able to provide high-quality annotations within the same day, for an appropriate price. I would definitely recommend to researchers in a similar position.

Melanie Senn Senior Machine Learning Engineer

Volkswagen Group of America’s Electronic Research Lab has a long history in automated driving research in the US and is working on future technology already today. During our collaboration, was highly focused on identifying our needs and providing great technical expertise. Being able to work with teams here in the Silicon Valley as well as in Germany was an enriching experience for all parties involved.

understand + ai

Our mission is to push the boundaries of technology to advance the state of
autonomous driving.

UAI's method of annotation

Traditional Manual Labeling

Our company was founded in the belief that a human’s ability to view and interpret an object in a particular way, can be complemented, enhanced and accelerated by applying artificial intelligence to a highly repetitive task. provides high-quality training and validation data to enable mobility companies to develop with confidence computer vision and machine learning models that reliably and safely power autonomous vehicles. The opportunity to enable new modes of mobility and give consumers increased choice inspires us everyday.

Our advanced capabilities include bounding box annotations and semantic segmentation for 2D camera data as well as LiDAR footage, with specific meta-attributes and instance ID labeling for object tracking.

By applying specialized artificial intelligence technology to repetitive tasks and quality check every single image with our in-house multi-level quality assurance team, we are able to quickly and precisely annotate data and thus accelerate the production of the ground truth data required to make autonomous driving a reality. is headquartered in Karlsruhe, Germany and has offices in Berlin and San Jose. Our engineers have relevant experience gained at innovative companies such as BMW, Google and Mercedes-Benz. Contact us today to better understand the road ahead.

Join us

Want to turn science-fiction into reality and make self-driving cars become part of our daily life while also working and having fun with super awesome people? We’re hiring. If you belong to the forefront of technology shaping the future, enjoy collaborating with curious and creative people, and believe you make a difference, apply today.

See open positions

Let's talk mobility

Passionate about accelerating the state of the art in autonomous driving, the ability of machine learning to improve reliability and safety, advanced approaches to the annotation of vast amounts of data? You get the idea. Head over to our blog, Let's Talk Mobility, and join the discussion.

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Launch your next autonomous driving project with high quality training and validation data. Contact us today so we can provide you with a customized solution which meets your needs.

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Our offices
Karlsruhe (HQ)

Hirschstr. 71
76133 Karlsruhe

San Jose

2540 N. 1st Street, Suite 201
95131 San Jose, CA