We are

With our proven track record in bringing autonomous driving to the road, we create a cutting-edge solution for the logistics sector.


The team met at the Technical University of Munich during their doctoral studies at the Institute of Automotive Technology. They worked on the research project “Autonomous Racing” initiated by Professor Markus Lienkamp.

A world-class team of researchers and developers

Autonomy is here to stay.

driveblocks is founded and operated by the 2021 winners of Indy Autonomous Challenge. Our team has many years of experience in artificial intelligence and autonomous driving software.

Dr. Stephan Matz

Managing Director

Stephan works on the commercialization of the autonomy algorithms and leads the company operations. He brings seven years of industry and leadership experience from various positions as a system engineer, project manager and team leader for a well-known Tier-1 automotive supplier. After his PhD on vehicle concept optimisation, he founded a consulting company with Prof. Dr. Markus Lienkamp and Dr. Peter Burda.

Dr. Alexander Wischnewski

Managing Director

Alexander is responsible for the product strategy of the driveblocks autonomy platform and its overall design and architecture. Prior to driveblocks, he was the team leader and system architect of the winning Indy Autonomous Challenge team of the Technical University of Munich. In addition, he designed vehicle motion control systems during his time as a research assistant at the Chair of Automatic Control and has a strong background in software engineering and robotic systems implementation.

Dr. Tim Stahl

Environment Model Lead

Tim leads the design and development of the sensor fusion and environment model algorithms which ensure that the driveblocks autonomy platform can achieve maximum safety and redundancy. During his time as a research associate at the TUM, Tim was member of the Indy Autonomous Challenge winning race team. He has been involved in programming automated systems for over 10 years and focused on lidar localization, trajectory planning, and safety during his PhD. The technical know-how is complemented by more than five years of industry experience

Dr. Felix Nobis

Perception Lead

Felix makes the driveblocks platform see, sense and perceive and brings deep-learning algorithms from research to production. As a research associate at TUM , Felix was responsible for perception pipelines of the winning Indy Autonomous Challenge vehicle. His research revolved around sensor fusion and robust perception for autonomous vehicles. Prior to this, he strenghtened his knowledge through studies and working experience at UC Berkeley, TUM, UP Valencia and BMW.

Leonhard Hermansdorfer

Simulation Lead

Leonhard designs and implements the virtual test and validation strategy and makes sure we achieve our validation goals. He designed the Software- and Hardware-in-the-loop simulation environments for the Indy Autonomous Challenge and did research on tire-road friction potential analysis during his time as a research assistant. Next to his technical expertise, he brings work experience in automotive industry and motorsports, as well as a strong network through his membership at the Bavarian Elite-Academy.

Dr. Thomas Herrmann

Motion Planning Lead

Thomas spearheads the development of the planning and decision making software modules in the driveblocks autonomy platform. Prior to this, he was deputy team leader of the TUM Indy Autonomous Challenge team and did research on real-time embedded motion planning and energy strategy optimization at the Chair of Automotive Technology and the Eindhoven University of Technology.

How we develop the driveblocks platform:


Our software development process is based on norms for safety-critical systems (ISO 26262, ISO/PAS 21448) and targets compliance with European and international legislation for autonomous vehicles. Each component meets real-time requirements and are subject to thorough scrutiny prior to release.

Modular microservice architecture

Breaking down the task of autonomous driving into hundreds of small components enables our customers to build customized solutions without re-inventing the wheel. In addition, this enables a continuous development and improvement process – shipping new features and performance improvements regularly.


Our components work with various compute platforms such as x86, ARM or classical microcontrollers. They utilize data from LIDAR, RADAR, and Camera sensors from different manufacturers. No lock-in to a limited set of hardware solutions.

Virtual edge-case generation

driveblocks software components are tested intensively in realistic simulation environments. This ensures safety, high availability, and ensures that they are ready to handle rare edge-cases. In addition, this increases the training data available for our AI models.

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