## 7 Conclusions  
With the evolution of vehicle technology and the appearance of advanced C-ITS architectures, the importance of modeling and evaluation of V2X-based ADAS applications have grown. The ADAS application framework presented in this paper provides a powerful yet straightforward environment for application testing, relying on the accurate network and protocol models of Artery/OMNeT++. It can be seen from the showcase results that the system has excellent potential in evaluating advanced V2X-based ADAS use case applications. Thanks to the declarative programming interface and the flexible build-up, one can model the widest scale of ADAS solutions in the framework, in which effects of ADAS actions in various abstraction levels and scopes can be characterized, the related perception and V2X communication indicators can be collected and the findings quantified in the form of statistically relevant numerical results. The flexibility of the framework makes the simulation of V2V (e.g., emergency vehicle warning, intersection collision warning) and I2V (e.g., roadworks warning, signal violation warning) safety application scenarios possible. With further enhancements and extensions of the framework, autonomous driving use cases could be fully supported as well.  
During the testing of the framework, a platooning and a road works warning use case were implemented and multiple key performance indicators, in functions of various V2X penetration levels, evaluated. The sample applications showed that novel applications, such as emergency braking, DENM generation, data aggregation, or maneuvering, can conveniently be implemented and examined using the ADASApp features.  
As part of our future work, we plan to build even more realistic sensor and environment models for V2X-supported ADAS mechanisms by integrating Artery with CARLA (the \"open-source simulator for autonomous driving\") so that the ADASApp-specified use cases could be examined based on practical sensor data processing pipelines with accurate object detection, decision, and fusion algorithms.  
Conceptualization, A.W. and L.B.; methodology, A.W. and L.B.; software, A.W.; validation, A.W.; original draft preparation, A.W., A.E. and L.B.; review and editing, A.W., A.E. and L.B.; supervision, A.E. and L.B.; project administration, L.B.; funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Cooperative Doctoral Program of the National Research, Development and Innovation Fund of Hungary (NVKDP-2021) and by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Hungarian Artificial Intelligence National Laboratory.  
Not applicable.  
Figure 13: The total number of detected objects in the different scenarios.  
Data Availability Statement: Not applicable.  
**Acknowledgments:** The authors would like to express their sincere thanks to [PERSON] at Commsignia for his continuous support, useful discussions and valuable comments. We would like to thank Commsignia for their support in the funding and their expertise.  
**Conflicts of Interest:** The authors declare no conflict of interest.