Synthetic Training & Validation Data
BIT provides training data sets as ground truth for machine learning and validation with highly scalable scene generation from real world scenarios. Different sensor outputs as well as corner case variations are produced with a high degree of automation. AI based & traditional methods produce scenes for this virtual simulation data.
BIT provides a virtual sensor simulation framework fully linked to ISO 26262 ASIL levels. Critical corner cases as well as automatic FMEDA output are processed.
Multiple sensors and different output formats are available and can be adapted quickly on demand. We generate content for camera sensors, radar (in preparation) and laser/LIDAR. Optical flow and sceneflow analysis is available on request.
The BIT digital content framework allows for automatic generation and randomized variation of scene aspects and behavior models.
We provide one of the largest industry sets for training data. Different production tools such as Unreal Engine™ as well as own render engines are used.
Real World Labeling
BIT takes pride in having a proven history of image labeling and scene annotation for different car OEMs. We are a key supplier for labeling 2D, 3D bounding boxes as well as annotation based on combined inputs such as LIDAR, radar and camera. Data protection compliant according to the EU General Data Protection Regulation.
Self-learning algorithm for automatic processing of camera data in unleashed quality. Delivery of segmented pictures and meta data in open JSON/XML field formats.
Synthetic dataset requirement specifications
The Scene Flow is offered in different file formats and as colored image for reference checks. We support open file formats for easy adoption into existing tools. Sub pixel accuracy along with selectable resolutions are supported.
Along with the segmentation of the virtual scene we generate a depth map which is shown here a visual graphic.
Independent of the Scene Flow an Optical Flow output engine is integrated in our pipeline allowing cross checks of the Scene Flow with same open file formats.
Localization / odometry
For cross check and implementation of odometry algorithms dedicated visiual odometry markers are implementable.
Along different groups and color conventions such as City Scapes. Level of detailed up to each pixel with unique identifiers.
BIT SW products detect and describe free space with a consisten file format which looks in a visual form like this:
Traffic sign detection
Growing catalog of international traffic signs & lights along with variations on effects such as dirty, damage, oclusion, bending, painting and others. We these variations critical corner cases are generated to increase the covergae of the learning.
Fast adoption of synthetic models to offer corner case variations based on real world inputs. Easy adoption of camera parameters, resolution, dynamic car behavior to debug machine learning.
– Altheimer Eck + Variations and extensions
Germany / Switzerland / USA / Urban highway with divider / Highway entrance ramp and exit / Toll station / Mountain-road / Seaside / Forest / Desert
– inner and outer Parkring + variations and extensions
NCAP scenarios / NCAP Emergency Brake Test
– Animation can be done with behavioral models (in research with partners)
Partly occluded objects / early detection of suddenly appearing objects
Partly occluded pedestrian
Partly occluded cyclist
Partly visible car / Bicycle
Child running onto street / objects falling onto the street
Detecting people’s poses/gestures
Cyclists giving hand signals for turning left/right / Policeman giving hand signals / Pedestrian behavior prediction
Unknown objects which weren’t trained in standard use cases
Animals (non stationary) / Unknown objects / obstacles (non stationary) / Unknown objects / obstacles (stationary)
“Soft” unknown objects / obstacles (stationary) – overdrivable / Objects hard to detect / Accidents
Static objects / content
Unknown static objects / obstacles
“Soft” unknown objects / obstacles – overdrivable
Truck / Tram / Special vehicles / Trailer
Animals / Unknown dynamic objects
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