This paper presents an open-source simulation framework for evaluating static and dynamic occlusions in urban environments, focusing on the challenges faced by Connected and Automated Vehicles (CAVs). It utilizes SUMO for traffic simulation and Python for ray tracing, enabling detailed analyses of occlusion effects without the need for complex co-simulation frameworks. Additionally, the framework introduces the observer vehicle type Floating Bike Observer (FBO), accounting for the increasing diversity of sensor-equipped vehicles in urban environments and enabling the investigation of further concepts for cooperative perception of CAVs in urban scenarios. A case study aimed at providing insights for the accurate calibration of the recently introduced Level of Visibility (LoV) metric by exploring further infrastructural and traffic-related influencing factors. The results reveal a strong sensitivity of the LoV metric towards traffic volumes and observer speed. Based on these findings, methodological adaptions of the LoV metric are proposed and discussed, such as a demand-dependent adjustment of the LoV scale and the inclusion of further influencing factors into the ray tracing method and subsequent assessment of visibility scores. Future work will deal with the optimization of the existing framework and the implementation of further applications. Furthermore, the calibration of the LoV metric will be further investigated by considering additional relevant infrastructural and traffic-related influencing factors.
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This paper presents an open-source simulation framework for evaluating static and dynamic occlusions in urban environments, focusing on the challenges faced by Connected and Automated Vehicles (CAVs). It utilizes SUMO for traffic simulation and Python for ray tracing, enabling detailed analyses of occlusion effects without the need for complex co-simulation frameworks. Additionally, the framework introduces the observer vehicle type Floating Bike Observer (FBO), accounting for the increasing div...
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