The transition from manually controlled vehicles to autonomous vehicles is in full swing. Large-scale, publicly usable pilot fleets have shown that the algorithms for autonomous driving have already reached a high level of maturity. However, a number of accidents in the context of these pilot fleets have also shown that there is still a need for further research in the area of safety.

Through his dissertation, Martin Stoffel presents an early indication of a fail-operational E/E architecture for autonomous vehicles that can both detect and mask random hardware failures.

In connection with this successful doctoral thesis, there was an excellent collaboration with Torc Robotics. The company, for which Martin Stoffel works, is an independent subsidiary of Daimler Truck and specializes in hardware and software for autonomous driving systems for long haul commercial vehicles in North America.