"Traffic State Prediction: a Value Added Service for Automated Driving Operations"

Traffic State Prediction: a Value Added Service for Automated Driving Operations
Henri Palm
1*1*,
Han Zwijnenberg 2 , Luc Wismans 2 3
1.DAT.Mobility , the Netherlands, *hpalm@dat.nl
2. Goudappel Coffeng , the Netherlands
3. University of Twente, the Netherlands

Abstract
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
curren t 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 nonurban networks. Including
predicted traffic states beyond the range of the on-board vehicle sensors offers a value adding service
for in vehicle deci sion making to achieve comfortable driving operations and to extend road safety

Keywords:
Automated driving, Short term prediction, Traffic control