The development of autonomous driving systems cannot be achieved without the application of artificial intelligent (AI) systems. In Parasuraman et al (2000), It has been suggested that automation of systems shows parallels to the Human Information process. We will elaborate on this assumption in two steps. First, to integrate the Human Information Processing model (HIP) with the model of Rasmussen’s Human Performance model (1983). Second, to apply this integrated model on Technology Information Processing (TIP). We will show where they are similar and where they differ. Furthermore, we will argue that AI systems on a Knowledge Based level are not likely to occur in the near future@en

Traffic congestion causes important problems such as delays, increased fuel consumption and additional pollution. In this paper we propose a new method to optimize traffic flow, based on reinforcement learning. We show that a traffic flow optimization problem can be formulated as a Markov Decision Process. We use Q-learning to learn policies dictating the maximum driving speed that is allowed on a highway, such that traffic congestion is reduced. An important difference between our work and existing approaches is that we take traffic predictions into account. A series of simulation experiments shows that the resulting policies significantly reduce traffic congestion under high traffic demand, and that inclusion of traffic predictions improves the quality of the resulting policies. Additionally, the policies are sufficiently robust to deal with inaccurate speed and density measurements.@en