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:铁路工业正在寻找新的方法,使一些复杂的列车功能自动化,例如物体探测、跟踪差别和准确的列车定位等,这要求通过不同类型的传感器,包括照相机、激光雷达、轮式编码器和惯性测量装置,对铁路环境进行人工认识。处理这种感官数据的有希望的方法是使用深层学习模型,这些模型证明在其他应用领域,例如机器人和自驾驶汽车取得了优异的性能。然而,测试新的算法和解决办法需要提供大量标签数据,这些数据是在不同的情景和操作条件下获得的,而由于在进入轨道边基础设施以及用所需的传感器装备火车方面的严格规定和实际限制,很难在真正的铁路环境中获得。为了解决这些困难,本文件提出了一个视觉模拟框架,能够在虚拟环境中产生现实的铁路情景,并自动产生耐性数据,以及由模拟的LDAR和照相机制作的标签数据集,用于培训深层神经网络或测试创新的算法。报告了一系列实验结果,以显示拟议方法的有效性。</s>