Traffic state prediction: A value added service for automated driving operations

The target in the PRYSTINE project is to realize Fail-operational Urban Surround perceptION (FUSION), which is based on sensor fusion, and control functions in order to enable safe automated driving in urban and rural environments. Estimating the (near) future traffic conditions ahead provides the automated driving controller with enhanced information to better and more comfortably act in the current situation. Significant improvements of quality and availability of data offers the opportunity to provide such information. By combining data science and traffic modelling techniques, an application is developed consisting of current and short term traffic prediction (typically up to 10 minutes ahead) and a virtual patrol detecting congestion and incidents for urban and non-urban networks. Including predicted traffic states beyond the range of the on-board vehicle sensors offers a value adding service for in-vehicle decision making to achieve comfortable driving operations and to extent road safety