Physics Informed Perception for Autonomous Driving (PIPER-AD)

Focus:	Artificial Intelligence Talents
Type of funding:	Individual funding programmes
Programme:	CZS research boost
Funded institution:	Stuttgart University of Applied Sciences

Prof. Antje Muntzinger, Professor of Computer Vision at HfT Stuttgart, is investigating the development of a multimodal AI system for the robust detection and tracking of vehicles on highways. She combines AI with prior physical knowledge to compensate for sensor errors.

Goals

Modern AI models for detecting the surroundings of autonomous vehicles deliver impressive results. However, they are often sensitive to data noise, occluded objects and difficult visibility or weather conditions. Implausible results can occur, especially with single-image predictions, which often have to be corrected using downstream processes.

However, highly automated driving systems require a robust and reliable perception of other road users through the combination of different sensor modalities.

The "PIPER-AD" project aims to develop an AI-supported perception system for highly automated driving systems. It is pursuing a hybrid approach by combining multimodal deep learning models with prior physical knowledge. Physical framework conditions, such as realistic vehicle dynamics and movement along the road, are integrated into the training process in order to increase the robustness and generalizability of the system.

In the long term, the framework is to be expanded to include uncertainty quantification in order to increase safety even under difficult conditions and the acceptance of autonomous vehicles.