提出快速评估道路传感器布局的指标体系，提升智能路口感知效率。
InSPE: Rapid Evaluation of Heterogeneous Multi-Modal Infrastructure Sensor Placement
- 设计覆盖、遮挡、信息增益三类指标，评估多模态传感器感知效果。
- 在CARLA仿真中构建数据集，支持多种路口与环境下的大规模评估。
- 适合智能交通系统设计者，用于优化复杂场景下的传感器部署。
基础设施感知对交通安全热点（如交叉口）的交通监控至关重要，也是自动驾驶协同感知的基石。尽管车载感知研究广泛，但基础设施感知仍面临多样路口结构、复杂遮挡、交通状况变化及光照、天气等环境因素的挑战。为此，本文提出异构多模态基础设施传感器布局评估方法InSPE，通过融合传感器覆盖、感知遮挡和信息增益三类指标，快速评估不同基础设施与环境组合下的感知效能。为支持大规模评估，我们在CARLA仿真环境中开发了数据生成工具，并构建了覆盖多种路口类型与环境条件的Infra-Set数据集。基于前沿感知算法的基准测试表明，InSPE可实现高效、可扩展的传感器布局分析，为智能交叉口基础设施优化提供可靠方案。
原文摘要 · Abstract (English)
Infrastructure sensing is vital for traffic monitoring at safety hotspots (e.g., intersections) and serves as the backbone of cooperative perception in autonomous driving. While vehicle sensing has been extensively studied, infrastructure sensing has received little attention, especially given the unique challenges of diverse intersection geometries, complex occlusions, varying traffic conditions, and ambient environments like lighting and weather. To address these issues and ensure cost-effective sensor placement, we propose Heterogeneous Multi-Modal Infrastructure Sensor Placement Evaluation (InSPE), a perception surrogate metric set that rapidly assesses perception effectiveness across diverse infrastructure and environmental scenarios with combinations of multi-modal sensors. InSPE systematically evaluates perception capabilities by integrating three carefully designed metrics, i.e., sensor coverage, perception occlusion, and information gain. To support large-scale evaluation, we develop a data generation tool within the CARLA simulator and also introduce Infra-Set, a dataset covering diverse intersection types and environmental conditions. Benchmarking experiments with state-of-the-art perception algorithms demonstrate that InSPE enables efficient and scalable sensor placement analysis, providing a robust solution for optimizing intelligent intersection infrastructure.
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