In autonomous driving, data from laser, camera and radar sensors in the car must be reliably and quickly combined and processed. Through this sensor data fusion and intelligent object recognition, the vehicle always has a precise image of the real traffic conditions, can locate itself in this environment and make the right decision in any driving situation on the basis of this information. The data to be processed for the environment recording is so complex that artificial intelligence methods are required to ensure a high level of traffic safety.
Fraunhofer IIS and its partners in the KI-FLEX project are developing a high-performance hardware platform and the associated software framework for this purpose. The algorithms used for sensor signal processing and sensor data fusion are largely based on neural networks and allow the vehicle position and environment to be recorded quickly and accurately.
The significance and usability of individual sensors varies depending on the traffic situation, weather and lighting conditions. In order to do justice to this, the platform is designed as software-programmable and reconfigurable hardware. This means that the algorithms used for sensor evaluation can be exchanged during the journey if conditions change. This allows the car to react flexibly to impairments or even the failure of individual sensors. In addition, the project team will develop suitable methods and tools to ensure the functional safety of the AI algorithms used and their interaction even during reconfiguration while driving. For the efficient execution of all algorithms and reconfigurations, the computing resources of the hardware platform are dynamically allocated according to the load.