Complex inner-city junctions are among the most critical traffic areas for
injury and fatal accidents. The development of highly automated driving (HAD)
systems struggles with the complex and hectic everyday life within those areas.
Sensor-equipped smart infrastructures, which can communicate and cooperate with
vehicles, are essential to enable a holistic scene understanding to resolve
occlusions drivers and vehicle perception systems for themselves can not cover.
We introduce an intelligent research infrastructure equipped with visual sensor
technology, located at a public inner-city junction in Aschaffenburg, Germany.
A multiple-view camera system monitors the traffic situation to perceive road
users' behavior. Both motorized and non-motorized traffic is considered. The
system is used for research in data generation, evaluating new HAD sensors
systems, algorithms, and Artificial Intelligence (AI) training strategies using
real-, synthetic- and augmented data. In addition, the junction features a
highly accurate digital twin. Real-world data can be taken into the digital
twin for simulation purposes and synthetic data generation.

本文介绍了在德国阿沙芬堡一个公共城市交叉口设立的智能研究基础设施，它配备了可视传感器技术和数字孪生，在数据生成、评估新的高度自动化驾驶传感器系统、算法和使用真实、合成和增强数据的人工智能培训策略的研究中发挥着关键作用。

智慧基础设施：一个研究交汇点

Smart Infrastructure: A Research Junction

Inner-city intersections are among the most critical traffic areas for injury
and fatal accidents. Automated vehicles struggle with the complex and hectic
everyday life within those areas. Sensor-equipped smart infrastructures, which
can cooperate with vehicles, can benefit automated traffic by extending the
perception capabilities of drivers and vehicle perception systems.
Additionally, they offer the opportunity to gather reproducible and precise
data of a holistic scene understanding, including context information as a
basis for training algorithms for various applications in automated traffic.
Therefore, we introduce the Infrastructural Multi-Person Trajectory and Context
Dataset (IMPTC). We use an intelligent public inner-city intersection in
Germany with visual sensor technology. A multi-view camera and LiDAR system
perceives traffic situations and road users' behavior. Additional sensors
monitor contextual information like weather, lighting, and traffic light signal
status. The data acquisition system focuses on Vulnerable Road Users (VRUs) and
multi-agent interaction. The resulting dataset consists of eight hours of
measurement data. It contains over 2,500 VRU trajectories, including
pedestrians, cyclists, e-scooter riders, strollers, and wheelchair users, and
over 20,000 vehicle trajectories at different day times, weather conditions,
and seasons. In addition, to enable the entire stack of research capabilities,
the dataset includes all data, starting from the sensor-, calibration- and
detection data until trajectory and context data. The dataset is continuously
expanded and is available online for non-commercial research at
this https URL

通过使用受智能公共城市交叉口内的视觉传感器技术的德国，加上多视角相机和雷达系统感知交通情况和道路用户的行为等多种传感器来收集数据，以完整掌握场景信息并提供基础训练算法，在自动交通中实现了文中所提出的智能公共城市交叉口，用以扩展驾驶员和车辆感知系统的感知能力，并且为自动交通的各种应用程序提供了底层数据支持。