The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, which require the artificial perception of the railway environment through different types of sensors, including cameras, LiDARs, wheel encoders, and inertial measurement units. A promising approach for processing such sensory data is the use of deep learning models, which proved to achieve excellent performance in other application domains, as robotics and self-driving cars. However, testing new algorithms and solutions requires the availability of a large amount of labeled data, acquired in different scenarios and operating conditions, which are difficult to obtain in a real railway setting due to strict regulations and practical constraints in accessing the trackside infrastructure and equipping a train with the required sensors. To address such difficulties, this paper presents a visual simulation framework able to generate realistic railway scenarios in a virtual environment and automatically produce inertial data and labeled datasets from emulated LiDARs and cameras useful for training deep neural networks or testing innovative algorithms. A set of experimental results are reported to show the effectiveness of the proposed approach.

翻译：铁路行业正寻求自动化众多复杂列车功能的新方法，包括目标检测、轨道识别及精确列车定位，这些功能需通过不同类型的传感器（如摄像头、激光雷达、轮式编码器及惯性测量单元）实现对铁路环境的人工感知。处理此类传感数据的一种有前景的方法是采用深度学习模型，该模型已在机器人及自动驾驶汽车等其他应用领域展现出卓越性能。然而，测试新算法与解决方案需要大量不同场景及运行条件下获取的标记数据，而在真实铁路环境中，由于严格监管及在接入轨道基础设施、为列车配备所需传感器时面临的实践限制，这些数据难以获得。为应对此类困难，本文提出一种可视化仿真框架，该框架能够在虚拟环境中生成逼真的铁路场景，并自动生成来自仿真激光雷达与摄像头的惯性数据及标记数据集，这些数据可用于训练深度神经网络或测试创新算法。实验结果表明了所提方法的有效性。