Artificial Intelligence

AI-Consulting & AI-Engineering

Through past and ongoing research and customer projects, we have built up a great expertise in the field of Artificial Intelligence. We are ready to share our knowledge in the AI sector and offer it either in a consulting package or directly in a development service package.

Together with you, we look at your individual use case and develop ideas & solutions to drive your projects forward.



Reference examples of our research and engineering applications

Scenario detection


When managing test drives, the automated, in-vehicle assignment and categorization of recorded data is a great benefit. The detection of objects and whole scenarios is increasingly based on methods of artificial intelligence and enables the pre-labelling of data already during the test drive. Therefore, the processing and analysis of data is supported significantly. In addition, the labelled data can also be used for online campaign management.


Anomaly detection


During validation and development of new driving functions it is very interesting to detect data that differs from regular characteristics (outlier detection). This data indicates anomalies, outliers and can indicate a possible mailfunction in the algorithm. Machine learning methods detect these anomalies, classify them automatically and support the analysis of their origin.


Image processing and time series analysis


Camera-based object detection and surrounding pose estimation offers a wide range of possibilities for the automation of various situations and targeted navigation. Our deep knowledge in classic and AI-based algorithms can support the successful development and realisation of new ideas, whether it is an ADAS or robot control system project.




For testing automated driving functions, it is necessary to modify target scenarios precisely and reproduce them uniquely. In HiL or SiL processes, there are many use cases, such as the insertion of virtual scenarios (e.g., with generative methods), development of individual SiL algorithms (e.g., GPGPU algorithms for reproducing DSPs), or intelligent management of HiL clusters.


For more information reach out to our experts

  • Metadata-Generation & Analysis: Parallel to data recording, recordings can be extended with metadata classified by machine learning algorithms. After that, the data can be specifically transferred to the cloud and be analyzed there. Intelligent pre-processing in the car (edge) enables a reduced amount of uploaded data.
  • Pattern Perception: There are various use cases for AI-based processing of sensor data: Direct pattern perception of sensor data, such as camera images, or high-performance processing in the cloud, where data from multiple vehicles can also be analyzed in combination (e.g., clustering or anomaly detection).
  • Explorative data analysis: Depending on your needs, we support you in all steps of data analysis:
    building suitable architectures and databases for processing and storage offline or in the cloud, through the selection of suitable questions, KPIs and algorithms, through to processing and visualization.
  • Perception: Perception of the environment, e.g. with cameras and lidar for precise localisation ans post estimation of objects, e.g. in logistics, road traffic, robot control, agriculture, etc.
  • Predictive Maintenance: Usage of data analytics for liefetime prediction, prediction of shutdowns and failure cases, e.g. in the area of commercial vehicles or industrial production systems.
  • AI in different systems: Integration of AI solutions in a wide variety of environments:

    + ROS
    + AWS / Azure
    + ECUs
    + HILs
    + Recordings / Measurement-Software (Aveto / ADTF / MTS)

Data Preprocessing

  • Image/video data
  • Time series, bus data
  • Radar/Lidar



  • Support Vector Machines
  • Convolutional Neural Networks
  • Sequence classification (LSTM-based NN)


Image Processing / Object Detection

  • Bounding Box Detection
  • Instance segmentation
  • Photogrammetry
  • Optical flow (Lucas-Kanade, Wavelets, NNs)



  • Support vector regression
  • Random forest regression
  • Pixel-by-pixel regression on image data (e.g. distance estimation)



  • DBScan
  • k-means


Feature Extraction

  • PCA
  • ICA
  • t-SNE
  • Recursive feature elimination


Anomaly detection

  • Variational/LSTM autoencoder



  • Tracking
  • Bayes / Kalman-filter

  • AWS / Azure
  • C/C++
  • C# /WPF
  • CUDA / OpenCL
  • Docker
  • Hadoop/Spark
  • Matlab/Simulink
  • MongoDB
  • MQTT
  • OpenCV
  • PostgreSQL
  • Python
  • ROS
  • scikit-learn
  • TensorFlow/Keras

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