Porsche

Porsche Engineering is an excellent example of a successful hub for innovation: The company participates in the AVEAS research project, focusing on securing highly automated driving functions through simulations. Using AI-driven analyses, critical traffic situations are to be identified based on sensor data and stored in a database. The objective is to generate various road models and traffic scenarios, which will be diversified to create a wide range of virtual test cases. Examples of critical moments in traffic include overtaking with insufficient distance, often resulting in near accidents. Porsche Engineering highlights the importance of safe responses from autonomous vehicles in such situations and emphasizes the ongoing intensive simulation activities. As part of their research, engineers deliberately intensify the difficulty of scenarios, such as reducing the distance between vehicles. Dr. Joachim Schaper, Head of AI and Big Data at Porsche Engineering, along with Tille Karoline Rupp, Responsible for Simulation, explain the development of a comprehensive catalog of critical scenarios to secure driver assistance systems and highly automated driving functions. The AVEAS project aims to overcome the lack of data, especially in critical traffic situations, which poses a challenge for autonomous driving. To address this gap, real drives are supplemented with simulations. However, there remains a shortage of real-world data for simulations, particularly in rare extreme situations during regular traffic. Porsche Engineering contributes crucial components, such as the JUPITER test vehicle for trials, equipped with various sensors transmitting data to the cloud. Advanced algorithms are utilized for data evaluation and identifying traffic participants and their behavior, continuously enhancing these processes.