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Acceleration-Based Automated Vehicle Classification on Mobile Bridges

机译：基于加速桥的自动驾驶汽车分类

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Mobile bridges have been used for a broad range of applications including military transportation or disaster restoration. Because mobile bridges are rapidly deployed under a wide variety of conditions, often remaining in place for just minutes to hours, and have irregular usage patterns, a detailed record of usage history is important for ensuring structural safety. To facilitate usage data collection in mobile bridges, a new acceleration-based vehicle classification technique is proposed to automatically identify the class of each vehicle. Herein we present a new technique that is based on the premise that each class of vehicles produces distinctive dynamic patterns while crossing this mobile bridge, and those patterns can be extracted from the system's acceleration responses. Measured acceleration signals are converted to time-frequency images to extract two-dimensional patterns. The Viola-Jones object detection algorithm is applied here to extract and classify those patterns. The effectiveness of the technique is investigated and demonstrated using laboratory and full-scale mobile bridges by simulating realistic scenarios.
机译：移动桥已被广泛用于包括军事运输或灾难恢复在内的各种应用。由于移动网桥可以在各种各样的条件下快速部署，通常只能保留几分钟到几小时，并且使用模式不规则，因此详细的使用历史记录对于确保结构安全非常重要。为了便于在移动桥梁中收集使用数据，提出了一种新的基于加速度的车辆分类技术，以自动识别每个车辆的类别。本文中，我们提出了一种新技术，该技术基于以下前提：每类车辆在通过此移动桥时都会产生独特的动态模式，并且可以从系统的加速度响应中提取这些模式。将测得的加速度信号转换为时频图像，以提取二维模式。这里应用Viola-Jones对象检测算法来提取和分类这些模式。通过模拟现实场景，使用实验室和大型移动桥来研究和证明该技术的有效性。

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