header object

Automotive

High-quality data labeling for the automotive industry - from the manufacturing and assembly process to training data for autonomous vehicles, including 3D Point Cloud annotations.

Talk to an expert

Expert Labeling
of Automotive Data

Image & Video Annotation for Autonomous Vehicles and the Automotive Industry.
Image & Video Annotation for Autonomous Vehicles
completed
projects
annotated
files

Keymakr provides professional data annotation for autonomous vehicles. Our experienced in-house annotation teams will ensure that your machine learning for self-driving car projects go smoothly.

Our proprietary annotation platform features a full suite of annotation techniques that can be adapted for your specific needs. Our annotators are comfortable working with all types, and qualities of data. We can also collect data for you from legal, open-source repositories, or even create bespoke data with our in-house studio.

Get In Touch

Automotive
Annotation Types

right line

01. Automatic Annotation

Using machine learning for fast and accurate annotation of your automotive data - the labeling process is curated by humans for accuracy.

get in touch
Automatic Annotation

02. Bounding Box

Among the most common types of annotation in the automotive industry, bounding boxes help you label objects quickly and efficiently.

get in touch
Bounding Box

03. Oriented Bounding Box

Helps us label target objects received from automotive images or videos with added precision by placing them at the right angle.

get in touch
Oriented Bounding Box

04. Cuboid

Simulates 3D qualities of an object such as weight and height. Useful to classify other cars or trucks in your datasets.

get in touch
Cuboid

05. Polygon

Outlines more complex and irregular shapes that don’t normally fit into other categories. Useful for more specific automotive data.

get in touch
Polygon

06. Semantic Segmentation

Classifies objects to carefully catalog entire scenes - including the whole highway, backgrounds, sky, and so on.

get in touch
Semantic Segmentation

07. Instance Segmentation

Creates individual labels for every single instance of your object, used for complex scenes where detail matters.

get in touch
Instance Segmentation

08. Skeletal

Represents a human with the help of connected lines - useful for classifying drivers, their posture, and position.

get in touch
Skeletal

09. Key Points

A granular method often used for individual parts of faces - it helps detect fine details such as emotions, tiredness, etc.

get in touch
Key Points

10. Lane

Annotates lanes and roads in given automotive data for proper recognition by your computer vision systems.

get in touch
Lane

11. Bitmap

Identifies separated parts of your image or video as belonging to the same object - useful for incomplete or complex scenes.

get in touch
Bitmap

12. 3D Point Cloud

Complex data with entire 3D environments where specific coordinates and spatial measurements matter.

get in touch
3D Point Cloud

13. Custom

Combines different types of annotation for your automotive data to perfectly match the specific needs of your training process.

get in touch
Custom annotation

Professional Data Annotation
Autonomous Vehicles

down-line

Get accurately labeled data for your computer vision systems in the automotive industry!

01 Annotation for in-cabin AI

Keymakr in-cabin applications in autonomous vehicles:

  • In-cabin behavior monitoring
  • Emotion recognition
  • In-cabin object recognition
  • Driver's assistant

02 Use Cases

  • Driver Monitoring Systems analyze multiple factors including road conditions, steering response, facial expression and gaze to determine if the driver is dozing off at the wheel.
  • Keymakr can annotate drivers’ faces and expressions up to a pixel-perfect level of detail.
  • Semantic segmentation of all objects in the car including people can help detect forgotten items or even pets forgotten in cars.
  • Skeletal annotation and movement tracking of the driver and passenger.
  • In-cabin AI is made possible by careful annotation. Keymakr’s unique project management systems empower developers by delivering valuable annotated video data quickly and at an affordable price.

03 Best Performing AI Starts with Accurately Annotated Data

We offer training visuals for self-driving cars, as well as custom image annotation solutions for autonomous vehicles and other AI-backed transportation systems.

A fully functioning and safe autonomous vehicle must be competent in a wide range of machine-learning processes before it can be trusted to drive on its own. From processing visual data in real-time to safely coordinating with other vehicles via IoT, the need for AI is essential. Self-driving cars could not do any of this without a huge volume of different types of training data, created and tagged for specific purposes.

To guarantee accuracy for your computer vision project, Keymakr utilizes three layers of human verification, followed by a final automated quality assurance check. By making use of multiple layers of verification it is possible to create error-free annotated training data for autonomous vehicle deep learning.

04 Artificial Intelligence Drives Intelligent Cars

The number of autonomous cars and algorithms being tested on the road increases yearly. Accurate perception of the driving environment requires enormous amounts of data to be captured and carefully annotated.

AI learns to recognize the surroundings, detecting vehicles and objects, roads, lanes, road signs, traffic lights, and other potential real-time hazards. Deep neural network algorithms may yet enable autonomous cars to drive better than human-driven cars, achieving safer and more effective transportation.

Overcoming data bias is critical for the success of AI in autonomous driving. Keymakr can play a part in troubleshooting persistent bias problems by creating and labeling varied, bespoke datasets. Our experienced teams of annotators can take on the burden of image and video annotation so that your data accurately reflects nighttime driving, low visibility weather, or road conditions in different countries.

Reviews
on

down-line
high perfomer
high perfomer emea
leader
star
star
star
star
star

"Delivering Quality and Excellence"

The upside of working with Keymakr is their strategy to annotations. You are given a sample of work to correct before they begin on the big batches. This saves all parties time and...

star
star
star
star
star

"Great service, fair price"

bility to accommodate different and not consistent workflows.
Ability to scale up as well as scale down.
All the data was in the custom format that...

star
star
star
star
star

"Awesome Labeling for ML"

I have worked with Keymakr for about 2 years on several segmentation tasks.
They always provide excellent edge alignment, consistency, and speed...

Data Annotation For Autonomous Vehicles

AI for autonomous driving automotive

Receive a complete analysis of the driver's behavior in the cabin including posture, emotional state and signs of fatigue to identify risks and improve road safety.

Quality inspection vision

High-precision annotations from cameras, LiDAR, and 3D point clouds are provided as a reliable basis for training autonomous control systems.

Driver monitoring AI system

Get a complete picture of the driver’s actions in the cabin from emotions and drowsiness to key postures and skeletal movements to improve passenger safety and prevent risks.

Predictive maintenance automotive ML

Use accurately annotated telemetry and video streams to train models that understand wear-and-tear of components and can perform damage assessments.

AI agent fine-tuning & validation

Receive custom-trained AI agents fine-tuned on domain-specific data with human-validated accuracy to optimize workflows and ensure compliance across industry-specific tasks.

FAQs

What is LiDAR 3D annotation and why is it relevant for autonomous vehicles?

LiDAR 3D annotation refers to the process of labeling 3D point clouds collected by LiDAR sensors. This includes identifying vehicles, pedestrians, road edges, etc with the goal of training AI models in spatial perception. This way systems can interpret their surroundings in three dimensions, dramatically improving object detection, distance estimation, and navigation. For low-light or adverse weather conditions, prevision is especially important. Major trends in 2025 emphasize AI-powered automatic LiDAR annotation, trajectory labeling, and use of synthetic data to reduce the amount of manual work.

How does ADAS data annotation contribute to vehicle safety?

ADAS data annotation involves tagging sensor data from cameras, radar, and LiDAR. This data teaches vehicles to recognize road signs, lane markings, cyclists, and other dynamic elements. We establish “ground truth” for machine learning models that assist drivers with features like lane-keeping, automatic braking, and adaptive cruise control. High-quality annotated datasets enhance real‑world adaptability, support regulatory compliance, and reduce prediction errors, all of which contribute to better assisted driving systems.

What are current trends in AI-assisted annotation for LiDAR and ADAS?

In 2025, annotation workflows are shifting in the following direction:

  • AI-driven pre-annotation reduces manual effort and speeds up labeling.
  • Semi-supervised learning uses both labeled and unlabeled data to improve efficiency.
  • Interpolation techniques enable high-quality tracking in sensor fusion scenarios by filling gaps between frames.
  • Timeline annotation - adding time to 3D data streams aids trajectory and motion prediction.

These trends support scalable annotation pipelines for autonomous systems.

What types of annotation techniques are used in autonomous driving?

Common annotation types include:

  • 2D/3D bounding boxes for basic object tagging.
  • Polygons and semantic segmentation for detailed shape mapping and road segmentation.
  • Instance segmentation for labeling discrete elements like lanes and crosswalks.
  • Keypoint and skeletal annotation for facial recognition and pose estimation in pedestrians/cyclists.
  • LiDAR point-cloud labeling for 3D object and movement detection.

These techniques collectively provide ADAS with robust environmental models.

How do large language models (LLMs) integrate with LiDAR and ADAS annotation workflows?

LLMs are increasingly used to auto-generate annotation instructions, assist quality assurance, and formulate annotation schemas for unstructured textual metadata. Sensor logs, driver behavior notes, and basic descriptions can all go through LLMs with relatively high accuracy.

While vision and sensor pipelines are still human-in-the-loop, LLMs help with documentation, error detection, and process automation - creating more efficient workflows across multimodal data types.