Fully automated and autonomous vehicles should be able to respond appropriately in every situation. Together with partners in the “KI-FLEX” project, the Fraunhofer Institute for Integrated Circuits IIS is developing a platform that uses artificial intelligence methods to help measure vehicle position and determine vehicle environment in the future.
As part of the “KI-FLEX” project, funded by the German Federal Ministry of Education and Research (BMBF), Fraunhofer IIS is leading the development of a software programmable and reconfigurable hardware platform that processes sensor data with AI-based methods for autonomous driving. The project is a key step in the development of technology components that are urgently required to make autonomous driving safe and reliable.
Autonomous driving is dependent on the fast and reliable processing and merging of data from laser, camera and radar sensors in cars. As a result, the vehicle always has an accurate picture of the actual traffic conditions, can locate its own position in this environment, and, on the basis of this information, make the right decision in every driving situation. The data that the vehicle must process to determine its environment is so complex that artificial intelligence methods are required to ensure a high level of safety on the roads.
To this end, Fraunhofer IIS and partners are working on the “KI-FLEX” project to develop a powerful hardware platform and the associated software framework. The algorithms used for sensor signal processing and sensor data fusion are largely based on neural networks and enable the vehicle’s exact position and environment to be determined.
Reconfigurable, secure and efficient
The relevance and usability of individual sensors varies depending on the traffic situation and on the weather and light conditions. To account for this, the platform is being designed as software programmable and reconfigurable hardware, which means that the algorithms used for sensor evaluation can be switched in line with changing driving conditions. This enables the vehicle to respond flexibly if individual sensors are compromised or if they fail. In addition, the project team will develop suitable methods and tools for ensuring the functional safety of the AI algorithms used and their interactions, even if the algorithms are reconfigured while the vehicle is on the road. To enable all algorithms and reconfigurations to be executed efficiently, the hardware platform’s computing resources are allocated dynamically according to load.
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