Performing Best: Organizing Suitable Human-AI Teaming for Improved Performance – An Experimental Study on Human Recognition Processes Under Time Pressure

The increasing integration of advanced driver assistance systems (ADAS) and artificial intelligence (AI) in modern vehicles highlights the importance of reliable object recognition in road traffic, especially when quick decisions need to be made in difficult visibility conditions. This study investigates the human error probabilities in identifying and distinguishing objects such as cyclists, pedestrians, and traffic light colors under suboptimal conditions. It also evaluates the performance of human-AI teams in the same scenarios, comparing human-first and AI-first decision workflows. The results provide insights into the reliability of these approaches and offer guidance for optimizing workflows to improve road safety and efficiency for this specific example but also a clear view to the optimization of human-AI teaming.