用欧洲新车安全评鉴场景评估道路设施辅助感知对行人安全的提升效果
CarlaNCAP: A Framework for Quantifying the Safety of Vulnerable Road Users in Infrastructure-Assisted Collective Perception Using EuroNCAP Scenarios
- 基于交通灯等设施部署传感器，突破车辆视角遮挡限制
- 在11,000帧的欧洲新车安全评鉴场景中实现最高100%事故规避率
- 为政策制定者提供量化依据，适合自动驾驶与智能交通研究者
近年来道路使用者数量增长显著，交通事故风险上升。弱势道路使用者（VRUs）尤其面临高风险，尤其在城市环境中常被停靠车辆或建筑遮挡。自动驾驶（AD）与集体感知（CP）是缓解该风险的有前景方案。特别是基础设施辅助的集体感知——在交通灯、路灯等设施上部署传感器——可提供更优视角，显著减少遮挡问题。为推动决策者采纳此技术，亟需全面的研究与数据集来证明其对VRUs的安全改善。本文提出一个针对VRUs的基础设施辅助感知安全评估框架，并构建了包含11,000帧的安全关键性欧洲新车安全评鉴（EuroNCAP）场景数据集（CarlaNCAP）。通过深入仿真研究，我们证明基础设施辅助的集体感知可在安全关键场景中显著降低事故率，相比仅配备33%感知能力的车辆，最高实现100%事故规避。代码已开源。
原文摘要 · Abstract (English)
The growing number of road users has significantly increased the risk of accidents in recent years. Vulnerable Road Users (VRUs) 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 or lamp posts, can help overcome perceptual limitations by providing enhanced points of view, which significantly reduces 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 the safety improvement by infrastructure-based CP specifically targeted at VRUs including a dataset with safety-critical 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 safety-critical scenarios, achieving up to 100% accident avoidance compared to a vehicle equipped with sensors with only 33%. Code is available at https://github.com/ekut-es/carla_ncap
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