The first public demonstrations with a full-scale autonomous vehicle prototype, the DevBot by Roborace, took place at the Berlin Formula-E circuit. The vehicle achieved a top speed of 150 kilometers per hour without a safety driver.
The participation in the Indy Autonomous Challenge challenged the team to add advanced AI algorithms to the stack, such as object recognition with LIDAR, Camera and RADAR sensors. The efforts were rewarded with a victory at the famous Indianapolis Motor Speedway against nine other international top universities.
The team confirmed its top position in the roster with a second place at the Autonomous Challenge @ CES in the following January, achieving overtakes at speeds of 265 kilometers per hour.
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.
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.
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
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 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.
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.