As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through applications that integrate diverse sensors with real-time decision-making. Streetscape applications-focusing on challenges like pedestrian safety and adaptive traffic management-depend on managing distributed, heterogeneous sensor data, aligning information across time and space, and enabling real-time processing. These tasks are inherently complex and often difficult to scale. The Streetscape Application Services Stack (SASS) addresses these challenges with three core services: multimodal data synchronization, spatiotemporal data fusion, and distributed edge computing. By structuring these capabilities as clear, composable abstractions with clear semantics, SASS allows developers to scale streetscape applications efficiently while minimizing the complexity of multimodal integration. We evaluated SASS in two real-world testbed environments: a controlled parking lot and an urban intersection in a major U.S. city. These testbeds allowed us to test SASS under diverse conditions, demonstrating its practical applicability. The Multimodal Data Synchronization service reduced temporal misalignment errors by 88%, achieving synchronization accuracy within 50 milliseconds. Spatiotemporal Data Fusion service improved detection accuracy for pedestrians and vehicles by over 10%, leveraging multicamera integration. The Distributed Edge Computing service increased system throughput by more than an order of magnitude. Together, these results show how SASS provides the abstractions and performance needed to support real-time, scalable urban applications, bridging the gap between sensing infrastructure and actionable streetscape intelligence.

翻译：随着城市人口增长，城市系统日趋复杂，推动着互联感知系统的部署以实现智慧城市的愿景。这些系统旨在通过整合多样化传感器与实时决策的应用，提升安全性、移动性和生活质量。聚焦于行人安全与自适应交通管理等挑战的街景应用，依赖于分布式异构传感器数据管理、跨时空信息对齐及实时处理能力。这些任务本质复杂且难以扩展。街景应用服务栈(SASS)通过三大核心服务应对这些挑战：多模态数据同步、时空数据融合与分布式边缘计算。通过将这些能力构建为语义清晰、可组合的抽象层，SASS使开发者能够高效扩展街景应用，同时最小化多模态集成的复杂性。我们在两个真实测试环境中评估了SASS：受控停车场和美国某大城市的交叉路口。这些测试环境使我们在多样化条件下验证SASS，证明了其实用性。多模态数据同步服务将时间错位误差降低88%，实现50毫秒内的同步精度；时空数据融合服务通过多摄像头集成，将行人车辆检测精度提升10%以上；分布式边缘计算服务使系统吞吐量提升超过一个数量级。综合结果表明，SASS提供了支撑实时可扩展城市应用所需的抽象层与性能表现，弥合了感知基础设施与可执行街景智能之间的鸿沟。