VERONET covers a broad spectrum of exemplary applications.

Connected, Cooperative, Automated Driving
  • The node control helps automated, assisted and traditional vehicles and traffic participants for an optimal, safe and efficient passage of an intersection.
  • The line control in combination with the vehicle control enables the use of optimal control strategies for an improved traffic flow, for example by platooning.
  • The net control helps automated and assisted traffic participants for the choice of the best routes and traffic modes.
  • The vehicle control delivers a set of fundamental functions in automated driving. Contrary to purely autonomous driving, the vehicles are integrated into a broad context of traffic. This is necessary when the benefits of automated driving with respect to traffic efficiency should really be brought into effect.
Traffic Planning
  • The mathematics behind traffic and environmental prediction is the same used in modern data mining. Therefore the development of mathematical models for traffic and environmental prediction can also be used to identify and quantify the principle dependencies and correlations in the traffic flows of a city.
  • The requirements management is used at all levels of the control hierarchy for the explicit collection and definition of the relevant traffic situations and the assignment for the desired traffic measures. These can then also be used for the direct training of the control objects. Thus traffic planning has direct influence on traffic control, by defining the control targets, which really can be followed.
  • Different traffic measures can be assessed and rated with the system development and rating as regards their effectiveness in traffic. This allows a controlled and targeted development of the right measures.
  • The new and flexible possibilities in the node, line, and net control through the simple definition and integration of new control actions and traffic measures allow an early consideration already in the planning and concept phase. A quick realization is already available at prototype level with realistic and performing functionality.
  • Incident detection can be used to identify and treat traffic situations, which have not already been considered for the calibration of the control but should have been considered. This is the basis for systematically grasping all the relevant traffic situations, which are used to train and adapt the control objects for steady improvements within the operation of the traffic control systems.
  • The example-based representation of the functional requirements for traffic control within the requirements management enables the quick and early identification, quantification and treatment of requirement conflicts. This normally leads to a much more consistent and realistic specification of the desired functionality resulting in less complex solutions with reduced efforts in development and implementation.
Traffic Control
  • Single crucial traffic junctions of a city can be controlled by the traffic node control according to the situation and in the best possible manner. Thus the source for the origin of congestion may be eliminated, in the ideal case jams can be avoided before they arise.
  • With the help of the traffic line control the main inflows and outflows of an inner city can be controlled by prioritization of certain directions, and /or conscious throttling. In combination with the net control the traffic flows into and out of a city can be deviated and controlled in a manner relevant to the situation.
  • Traffic and environmental prediction optionally in combination with incident detection enables the implementation of predictive control strategies to avoid congestion from the beginning.
  • The system development rating and the requirements management in combination with the example- based representation of functional requirements allow the quick and early identification of conflicts in the functional requirements. This is the foundation of a consistent and realistic specification with comparable prototypical applications, even if conventional control modules are ultimately implemented.
Traffic Detection
  • Machine Learning and pattern recognition procedures are the methods of choice within traffic and environmental prediction, allowing an efficient and effective identification and forecast of the relevant traffic situations based on arbitrary sensors and information.
  • Virtual sensors can convert existing sensor data into the according traffic values for uniform controls, allowing the best possible usage and integration of already existing and installed sensors. Virtual sensors can also be used even when only measurements with time restrictions are available.
  • Different sensor technologies (e.g. floating car data in combination with stationary sensors) can be combined in the form of sensor fusion with the help of virtual sensors. This allows the traffic situation to be assessed much more precisely, exploiting the advantages of the relevant sensor technologies or compensating their disadvantages.
  • The requirements management and the system rating make it possible to derive precise specifications of the necessary sensors to detect the relevant traffic situations for the control. With the help of special mathematical procedures an automated identification of the best possible sensors for a certain control problem can be achieved. This ensures that only the necessary values are measured based on the control requirements and assuring economically reasonable traffic sensor equipment.
  • Incident detection can be used to check sensors as regards their consistency and plausibility. Hereby incidents and potential sensor failures can be treated within a common software environment.
  • Incident detection can be used to identify new traffic situations, which have not yet been considered in the calibration of the control but for which the control should be adapted to improve control performance.
System Development
  • Arbitrary actions and controlled traffic measures (e.g. traffic lights, routing recommendations, warning messages, Car2X-communication, park & ride recommendations, etc.) can be controlled integrally within a common context in an overall control system at all hierarchy levels of the traffic node, line, and net control.
  • System rating allows the identification and quantification of the measures (additional actions, improved traffic sensors, definition of the control targets, usage of alternative control methods and parameterization, etc.) so that the best possible impacts on traffic can be achieved.
  • Requirements management and system rating allow the precise and consistent specification of necessary system components. This assures that only components are used and integrated which are really necessary and have any effect on the traffic.
Component Development
  • System rating allows an early and quick evaluation of the effectiveness of a system component within the overall system to guide a targeted development and estimate if the necessary expenditure on development is economically justifiable.
  • In the traffic node, line as well as in the net control, arbitrary actions and traffic measures (e.g. like Car2X-communication, information systems, etc.) can be integrated and adapted quickly and efficiently into the control procedures and models.
  • Arbitrary new sensors and information can be integrated easily and quickly into the traffic and environmental prediction as well as into incident detection. Virtual sensors can be used to convert or fuse new sensor data for a quick integration into existing or new traffic control.

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