Vehicle Control

One of the main expected advantages of automated and connected driving will be the improvement of traffic efficiency (beside passenger comfort and safety). The implementation of cooperative control strategies at the level of individual vehicles will enable a better use of the existing infrastructure by increasing the capacities through better coordination of the different traffic participants. Amongst others, congestion sources will be reduced with the help of more cooperative and predictive vehicle control procedures.

The VERONET vehicle control describes and realizes the necessary driving behavior of individual vehicles in such a way that traffic efficiency will be improved without significantly compromising the needs of individual traffic participants. The vehicle control is seamless integrated in the overall VERONET traffic control concept with all its advantages.

The VERONET vehicle control consists of

  • Control algorithms for the traffic- and energy-efficient approach and crossing of intersections in combination with the VERONET node control,
  • Control algorithms for the dedicated coordination of different vehicles (e.g. like platooning),
  • Predictive control strategies for driving using information from V2V and V2X communication.

The implementation and realization of VERONET vehicle control is carried out

  • in the form of advanced driver assistant systems (ADAS) and automated, connected vehicles, or
  • through appropriate specification of control strategies and targets from roadside units via V2X communication.


Features and Advantages

  • Predictive control strategies for the realization of cooperative driving to improve traffic quality
  • Dramatic reduction of the complexity of the whole overall traffic problem by the modularization into uniform control units, which are much easier to handle
  • Situation-aware control for better acceptance and effectiveness
  • Enabled implementation of individual strategies and control targets
  • Optimal usage of the individual capabilities of each vehicle to improve the traffic flow
  • Usage of (self-)learning procedures for a continuous improvement of the control functions
  • The decentralized architecture of VERONET makes it possible to easily extend existing networks and allow implementation scenarios where new VERONET objects are mixed with existing traditional traffic control equipment
  • Leveraging all possibilities coming from V2V and V2X communication
  • Accompanying, mature development process enabling the realistic effectiveness rating of different control strategies