What we do

To make autonomous driving as safe as possible we evaluate data for neural networks.

Testing data can be images or LiDAR point cloud maps. Our aim is to make sure that the neural network is trained with almost 100% flawless data. To reach this goal we write tools that help us to evaluate more precisely.

2D

Semantic Segmentation

Aims to make the class prediction for an entire input image. To reach this goal each pixel is labeled with the specified color of its object.

Full Image Tagging

Full image tags provide general information about an input image such as environment and weather conditions.

Bounding Boxes

Rectangles to enclose a specific object or region. Provides tracking of different classes like cars, pedestrians or signs.

Object Tagging

Object tags provide specific information about an input image. Every object has its own requirements that need to be considered.

Skeleton Annotations

Visualizes human movements for a static input image by annotating different body parts. Is used for predicting motion sequence of a pedestrian.

Road Model Segmentation And Tagging

Aims to make the lane prediction for an entire input image. To reach this goal each lane is labeled with the specified color.

3D

Semantic Segmentation

Aims to make the class prediction in 3D LiDAR space. To reach this goal each point is labeled with the specified color of its object.

Bounding Boxes And Object Tagging

Cuboids to enclose a specific object or region in 3D LiDAR space. Provides tracking of different classes like cars, pedestrians or signs. Object tags provide specific information about a LiDAR cloud. Every object has its own requirements that need to be considered.

A few things we’re great at

There are a few things that makes us special. At the very beginning we made sure that we can work with different companies in the future.

FLEXIBLE TOOL

Due to our good structured code of our evaluation tools we are prepared for changing requirements. Each company has its own practices we can handle.

CONCENTRATET EVALUATORS

We know how important it is to feed neural networks with correct data. A neural network can only be as good as the training data is. Autonomous driving is a very sensitive topic, we definitely want to make sure it is fed with verified correct data.

POWERFUL TESTING AREA

With just a few clicks we can detect incorrect data without even looking at them. Nevertheless we verify the data to enhance the correctness.

YEARS OF EXPERIENCE

We have experience in evaluating data since 2015. In these years we improved the methods to verify data more effectively.

Evaluation

2D Bounding Box
Object Tagging
Semantic Segmentation
3D LiDAR Bounding Box

Automatic Tests

Pre Evaluation
Testname: Relevance: Message: Details:
2DBBoxSpecification ERR BBox missing object is missing a bounding Box
2DBBoxSpecification ERR BBox missing object is missing a bounding Box
2DBBoxSpecification ERR BBox missing object is missing a bounding Box
2DBBoxSpecification ERR BBox missing object is missing a bounding Box
2DBBoxSpecification ERR BBox missing object is missing a bounding Box
2DBBoxSpecification ERR BBox missing object is missing a bounding Box
2DBBoxSpecification INFO 2DBBoxSpecificationTest failed Errors occured: 6 Tested: 3 files from: 3

Explanation: This is an automatic test that can be executed right before the evaluation. With intelligent methods of this tool you can find out the most trivial mistakes like missing bounding boxes, as you can see in the table above.

Runtime
Testname: Relevance: Message: Details:
3DBBox WARNING ObjectTag mismatch 13 not contained in the 3D space
3DBBox WARNING ObjectTag mismatch 14 not contained in the 3D space
3DBBox WARNING ObjectTag mismatch 12 not contained in the 3D space
3DBBox WARNING ObjectTag mismatch 11 not contained in the 3D space
3DBBox WARNING ObjectTag mismatch 9 not contained in the 3D space
3DBBox WARNING ObjectTag mismatch 8 not contained in the 3D space
3DBBox WARNING ObjectTag mismatch 16 not contained in the 3D space
3DBBox INFO 3DBBox succeeded Tested: 3 files from: 3

Explanation: This is an automatic test that is executed during the runtime. With intelligent methods of this tool you can find out the most trivial mistakes like non-matching object tags, as you can see in the table above.