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

Traffic state prediction services for automated driving and traffic management.

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. Estimation of the complete current and (near) future traffic conditions ahead, beyond the range of on-board vehicle sensors, provides the automated driving controller with enhanced information to act better and more comfortably in the current situation and to extent road safety. Traffic state prediction is also an important input for pro-active traffic management as identified within TM2.0 (Traffic Management2.0 vision ERTICO). The derivation of a common operational picture for traffic management and mobility service providers, like CAV, enables the collaboration between public and private parties in facilitating traffic. Stimulating and enhancing this collaboration is part of the Dutch innovation program MobilitymoveZ. 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

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Final results after Year 3

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3