CoRTSG: A general and effective framework of risky testing scenario generation for cooperative perception in mixed traffic

The individual perception capabilities of autonomous vehicles face significant challenges in overcoming occlusions and achieving long-distance visibility. Consequently, cooperative or collaborative perception (COOP), which can effectively expand the perception field and help to detect the human-driven vehicles or vulnerable road users by leveraging vehicle-to-everything (V2X) communication among connected and automated vehicles (CAVs) and roadside units (RSUs), has garnered increasing academic attention in recent years. Despite notable advancements in datasets, simulation platforms, and algorithms, there remains a dearth of research focused on the evaluation and testing methodologies for COOP systems, particularly concerning driving safety. This study proposes a general and effective framework for Risky Testing Scenarios Generation for Cooperative Perception (CoRTSG), which can integrate traffic data and prior knowledge to sequentially produce risky functional, logical, and concrete scenarios. Specific functional scenarios pertinent to COOP are extracted from the traffic crashes due to vision occlusion, thereby defining its operational design domain. Subsequently, by selecting appropriate sites on an OpenDRIVE map, risky logical scenarios are determined. A fast occlusion judgment algorithm is also developed, assigning roles to objects within a logical scenario and employing autoregressive sampling to derive risky concrete scenarios. Accordingly, a comprehensive large-scale library of risky testing scenarios encompassing 11 functional and 17,490 concrete scenarios for COOP in a mixed traffic environment with CAVs, non-CAVs, and vulnerable road users has been created for the first time in literatures. All concrete scenarios have been simulated in the CARLA environment, facilitating thorough testing of representative COOP algorithms in terms of detection accuracy, driving safety, and communication efficiency. The results highlight that COOP significantly enhances driving safety and detection accuracy compared to individual perception, however, further optimization is needed to balance performance with bandwidth requirements and to ensure stable safety improvements. Data and code are released at https://github.com/RadetzkyLi/CoRTSG.