Abstract

The growing number of road users has significantly increased the risk of accidents in recent years. Vulnerable Road Users (VRUs), who are most often involved in traffic incidents in urban environments, are particularly at risk, especially in urban environments where they are often occluded by parked vehicles or buildings. Autonomous Driving (AD) and Collective Perception (CP) are promising solutions to mitigate these risks. In particular, infrastructure-assisted CP, where sensor units are mounted on infrastructure elements such as traffic lights, can help overcome perceptual limitations by providing enhanced views and reducing occlusions. To encourage decision makers to adopt this technology, comprehensive studies and datasets demonstrating safety improvements for VRUs are essential. In this paper, we propose a framework for evaluating safety-critical infrastructure-based CP specifically targeted at VRUs including a dataset with EuroNCAP scenarios (CarlaNCAP) with 11k frames. Using this dataset, we conduct an in-depth simulation study and demonstrate that infrastructure-assisted CP can significantly reduce accident rates in EuroNCAP scenarios, achieving up to 100% accident avoidance compared to a vehicle equipped with sensors with 33%. Code is available at https://github.com/ekut-es/carla_ncap