Connected vehicular applications have recently become extremely important for addressing challenges in the fields of safety and efficiency on the road. However, the use of connected applications in vehicular environments is limited due to the unreliable nature of communication links. This motivates a search for new ways to improve vehicle-to-vehicle communications. The concept developed in this thesis addresses this problem by using data from the on-board perception system as an additional source of information to improve vehicle-to-vehicle communications. The use of positioning data from on-board sensors has shown its potential in railway, cellular and satellite communications systems for scenarios where one dynamic communication partner moves along a known trajectory. In this thesis, the information about the surrounding environment from the perception system of a modern automated vehicle is used to predict changes in direct-link communications among highly dynamic partners. The perceived information is assumed to be exchanged between the functional components of the automated vehicle, including the communication subsystem, via standardized interfaces. This approach enables a range of improvements via sensor-aided predictive communication algorithms. In this thesis, the sensor-based predictive compensation of Doppler frequency shift and the prediction of predominantly line-of-sight or non-line-of-sight communication conditions are investigated as two illustrative classes of sensor-aided algorithms. The results obtained show that the proposed methods outperform existing reference solutions in various scenarios and can be beneficially applied to different communication layers, data dissemination models, types of cooperative vehicular applications and driving scenarios.