Data fusion development for automated vehicles

BASELABS Create Embedded

BASELABS Create Embedded is a software library for the development of data fusion systems for automated driving functions on embedded platforms. It provides data fusion algorithms that combine data from radar, camera and lidar sensors. The resulting object fusion provides a unified object list of the vehicle's environment and serves as an input to path planning and decision making algorithms.

Benefits at a glance

  • Software library for the fast and efficient development of data fusion systems for series production
  • Reduces the costs of data fusion development by up to 50%: Read our White Paper
  • Provides dynamic object fusion for automated driving functions such as AEB, ACC and Highway Pilot
  • Supports all relevant automotive sensors like radar, camera and lidar
  • Scalable from radar-camera front fusion up to 360° object fusion
  • Consistent development workflow from prototyping with vADASdeveloper or ROS to series production, e.g. with AUTOSAR Classic/Adaptive
  • Support for typical development platforms, e.g. NVIDIA DRIVE
  • C source code for embedded hardware platforms, e.g. AURIX 2G, Renesas RH850 and ARM Cortex-R52
  • Series production ready (ASPICE)
  • Based on consistent, traceable and fully reviewed requirements, architecture, design and test documents
  • Full test coverage and code inspections
  • Graphical configuration of data fusion systems
  • Flexible and easy adaption of data fusion applications to different sensor-setups or -types


  • SAE level 0-3: Automated Emergency Braking (AEB), Adaptive Cruise Control (ACC), Forward Collision Warning (FCW), piloted driving
  • SAE level 3-4: Object fusion as part of a diverse redundant safety architecture, e.g. ASIL D decomposition


  • A real-world reference system shows that even simple systems generate development costs of at least EUR 1.2 million.
  • Standardized software libraries with tooling support significantly reduce efforts, saving up to 50% of costs while significantly shortening development times.

Download the White Paper in English or Japanese.

Consistent development workflow

BASELABS Create Embedded provides a consistent development workflow - from pre-development and prototyping to series production. Utilizing the tool makes the implementation of data fusion systems for embedded platforms faster and much more efficient. The development tool contains data fusion algorithms that combine data from radar, camera and lidar sensors. The resulting C source code can be used along the entire development chain. Thus, the development tool drastically reduces the development effort at every stage on the way to series production. BASELABS Create Embedded allows for safety compliant data fusion development, including documentation and testing for safety use cases.

Supported sensors

BASELABS Create Embedded supports a wide range of sensor technologies and object/track interfaces from various vendors, e.g. Continental ARS, Aptiv ESR, Bosch MRR, Mobileye EyeQ, Valeo Scala and Ibeo HAD.

Custom or extended sensors can be implemented and integrated using the software development kit (SDK).

To process low level data such as lidar point clouds or high-resolution radar images, the Dynamic Grid provides an alternative approach.

Data fusion library

Data fusion designer and generator

With the data fusion designer, the radar, camera and lidar sensors of the vehicle setup are configured, customized and parameterized. A specific data fusion system for object fusion is generated from this configuration.

Data fusion reference architecture

The integrated reference architecture for object fusion algorithms allows to build data fusion applications ranging from two sensor systems to large 360° setups with many sensors. The architecture can be customized and extended.


Data fusion library for embedded systems

The integrated data fusion library contains algorithms to build custom object fusion systems, e.g.

  • numerically stable Kalman filters,
  • data association methods,
  • sensor models,
  • existence probability handling,
  • track management algorithms,
  • classification fusion,
  • synchronization of asynchronous sensor measurements and
  • handling of out-of-sequence measurements (OOSM).

The C source code of the library is fully accessible
and ready for embedded platforms:

  • Compatible with relevant series hardware, e.g. Aurix 2G and Renesas RH850
  • Runtime- and memory-efficient
  • Customizable and extensible
  • Human readable and close to hand-written code
  • MISRA C:2012 compliant
  • Self-contained, no dependencies to external libraries

Middleware compatibility and integration

BASELABS Create Embedded is compatible to all middleware systems and provides a seamless integration of the generated data fusion algorithm to many platforms and runtime environments:

  • AUTOSAR classic/adaptive
  • bare metal environments
  • ROS
  • ADTF
  • RTMaps
  • Matlab/Simulink
  • vADASdeveloper
  • any custom middleware

Video introduction

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BASELABS Create Embedded Release 4.2 (31.01.2020)

New features

  • Support for NVIDIA DRIVE and ARM platforms: Many modern driving functions depend on AI and deep learning methods whose results are often fed into a data fusion. While AI-based methods require extensive parallelization and therefore often run on GPUs, object fusion can be sequentially executed on CPUs. With the support of the ARM CPU family, the object fusion of BASELABS Create Embedded can now be run on the ARM part of NVIDIA DRIVE platforms.
  • Built-in height estimation: Besides determining the width and length, BASELABS Create Embedded now provides a built-in way to estimate the object’s height if at least one of the configured sensors provides information about the height.
  • Access to classification when creating new tracks: In state proposers, it is now possible to reference the classification value or object type that might be provided by a sensor. With this, new tracks can be better initialized, e.g. its initial velocity range or uncertainty might be smaller for pedestrians than cars.


  • Data Fusion Designer: When upgrading a project, a backup copy of the templates is made to avoid loss of customizations.
  • Data Fusion Designer: Support for Visual Studio 2017 and 2019 when creating a project for building the data fusion library.


  • Data Fusion Designer: Font size for error messages is too small.
  • Data Fusion Designer: Error message for unset system model parameters does not disappear after setting this parameter.
  • vADASdeveloper component: Invalid data types for some of the C functions.
  • vADASdeveloper component: Out of sequence measurements cause out of sequence tracks even though deterministic buffering is used.
  • vADASdeveloper component: Template error when model properties do not have the display name attribute.
  • Installer: Incomplete uninstallation when multiple versions of BASELABS Create Embedded are installed.

BASELABS Create Embedded Release 4.1 (02.01.2020)


  • Template engine: Adding file headers may cause errors for some file types.
  • User manual: Underscores cannot be copied from the user manual.
  • Installer: Installer does not check for the needed .Net version.
  • Linux: User manual not contained in documentation folder.
  • ROS node: Message conversion stub contains error in local variable declaration.

BASELABS Create Embedded Release 4.0 (18.10.2019)

New features

  • Classification fusion: Sensors such as cameras often provide information on the class of a certain object. This information is highly relevant to improve the data fusion performance, e.g. to specify class-specific sensor characteristics. The Data Fusion Designer of BASELABS Create Embedded now supports to design data fusion systems that include classification information from any of the configured sensors. If multiple sensors provide a classification for an object, the built-in classification fusion resolves potential conflicts. Furthermore, the runtime visualization has been enhanced and includes the object classification.
  • Out-of-sequence measurements handling: When using multiple sensors, data may arrive delayed at the data fusion, e.g. due to pre-processing or communication. To handle these so-called out-of-sequence measurements, BASELABS Create Embedded now provides a deterministic buffering approach. When using this buffering method, the measurements of the different sensors are buffered and then processed in chronological order.
  • Runtime calibration: The parameters configured in the Data Fusion Designer now can be changed at runtime as well.
  • Visualization of ego motion data: The velocity and yaw rate of the host vehicle are shown in the runtime visualization.
  • Track statistics: For each configured sensor, the resulting track structure of the data fusion contains the information whether an object has been seen by this sensor for the last eight time steps.
  • Traceability: Generated files contain a header containing information on the time of creation and the used version of BASELABS Create Embedded.
  • Host vehicle Parameters: Dimensions of the host vehicle can be configured in the Data Fusion Designer and are used in the visualization during development and runtime.


  • vADASdeveloper component: The classes used at the component's pins check that the maximum number of measurements is not exceeded.
  • Unique error codes: The error codes contain information on their origin.
  • Association: When multiple system models are configured, the measurements from all continuous models are used for the measurement to track association.
  • Data fusion template: Less stack memory consumption to avoid stack overflow exceptions.
  • Example projects: ROS and vADASdeveloper example project estimate the object's width and object class.
  • Template dependencies: Dependencies between templates can be expressed. Missing dependencies are automatically added, when adding a new data fusion item.


  • vADASdeveloper example project: The example project cannot be built when the Trait-C data fusion template is used.
  • Runtime visualization: The fields of view are not correctly drawn but cover the whole visualization area.
  • Runtime visualization: Predicted measurements are shown even if a sensor is not active.
  • vADASdeveloper component: Large measurement data structures cannot be marshalled to C structures.
  • ROS node: Extended state spaces, e.g. by an additional width space, are not supported in the track message.
  • Linux: Custom modules not found when building a data fusion project on Linux machines.

BASELABS Create Embedded Release 3.0 (19.07.2019)

New features

  • Smart radar measurement model: The Data Fusion Designer now contains a 'Smart radar measurement model' which can be used for typical internally tracking radar sensors.
  • "Expert mode": The control flow of the data fusion is now available both in C and Trait-C. An introduction on Trait-C is included.
  • Improved model extensibility and reusability: Existing models can now be easily extended right from the data fusion designer, e.g. the width and the length of an object can be added while keeping or reusing an existing model. For that, additionally required quantities from other model parts can now be accessed and read by the extended model, e.g. the dynamic position of the object.
  • Visualization of object length and width: If an object contains width and/or length information, it is shown in the visualization. This is especially useful if one or more sensors provide width and/or length information, e.g. lidar or camera sensors.
  • Improved workflow for series development: The creation of data fusion projects for series production has been simplified and improved.


  • Multiple versions installable: Multiple versions of BASELABS Create Embedded can be installed on the same machine to enable the user to work on projects created with different versions of BASELABS Create Embedded.
  • Width estimation in vADASdeveloper example project: The object width is estimated and visualized in the vADASdeveloper example project.
  • vADASdeveloper example project: Increased replay speed.


  • Data Fusion Designer: Model names do not fit in the drop down.
  • Data Fusion Designer: Cannot generate code if deactivated sensor is not complete.
  • vADASdeveloper example project: Player does not pause on tracks.
  • Model wizards: Wizards for different models are not consistently formatted and documented.
  • Visualization: Sporadic stack overflow while zooming or panning.

BASELABS Create Embedded Release 2.0 (15.04.2019)

New features

  • Width and length estimation: The Data Fusion Designer now allows to add system models additionally to the motion model. Models for width and length estimation can be selected.
  • Model wizards: The Data Fusion Designer now provides wizards for creation of spaces, system models, measurement models and detection models.


  • Visualization: Associations and predicted measurements are shown.


  • vADASdeveloper example application: Camera image is too small.
  • vADASdeveloper example application: Silent fail on exception.
  • vADASdeveloper example application: Incorrect restoring of settings.
  • vADASdeveloper component: Pin names of component contain sensor ID.
  • vADASdeveloper component: Float not supported.
  • vADASdeveloper component: Incorrect out of sequence measurements handling.
  • Smart camera models: Insufficient descriptions of properties.

BASELABS Create Embedded Release 1.0 (01.03.2019)

Initial release

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