Training data for ai-backed autonomous driving

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.

Artificial Intelligence drives intelligent cars

The number of autonomous cars and algorithms being tested on the road increases yearly. Accurate perception of driving environment requires enormous amount 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 enable autonomous cars to drive better than human-driven cars, achieving safer and more effective transportation.

Annotation for in-cabin AI

Keymakr In-cabin applications in autonomous vehicles:

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

Use cases:

  • Driver Monitoring Systems analyze multiple factors as road conditions, steering response, facial expression and gaze to determine if the driver is dozing off at the wheel.
  • Keymakr would annotate the driver's face and expression up to pixel - perfect details.
  • Semantic segmentation of all objects in the car including people in order to detect forgotten items in the car.
  • Skeletal annotation and movement tracking of the driver and passenger.

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