Guerra Kevin, Raymundo Carlos, Silvera Manuel, Zapata Gianpierre, Moguerza Javier M
R &D Laboratory in Emerging Tehnologies, Universidad Peruana de Ciencias Aplicadas, Lima, 15023, Peru.
Ingenieria Civil, Universidad Peruana de Ciencias Aplicadas, Lima, 15023, Peru.
Sci Rep. 2024 Aug 26;14(1):19761. doi: 10.1038/s41598-024-70936-z.
The accelerated deterioration of roads is conditioned by parameters such as climate change, poor construction, and heavy vehicle traffic. Two relevant measures to monitor the condition of a road are the International Roughness Index (IRI) and the number of functional failures in a segment, mainly potholes, since they are associated with higher risks such as accidents or damage to vehicle mechanics. In the state of the art, pothole detection or International Roughness Index (IRI) calculation algorithms are proposed, but they use vehicles designed to produce less vibration and use phones that decrease the performance of the embedded sensors. In addition, some works propose complex algorithms of higher computational load that leads to use more hardware and power consumption. In this context, the present work aims to monitor the condition of a road through low-cost dedicated sensors implemented in an urban patrolling all-terrain vehicles (ATVs), where energy consumption is optimized using low-complexity signal processing techniques for noise reduction and detection algorithms. The results show an average accuracy of 90.5% in the detection of potholes, a relative error of 8.41% in the calculation of the International Roughness Index (IRI) and an average reduction of 65.4% in the monitoring time.
道路的加速恶化受到气候变化、施工质量差和重型车辆交通等参数的影响。监测道路状况的两个相关指标是国际平整度指数（IRI）和路段内功能性故障的数量，主要是坑洼，因为它们与事故或车辆机械损坏等更高风险相关。在现有技术中，提出了坑洼检测或国际平整度指数（IRI）计算算法，但它们使用旨在产生较少振动的车辆，并使用会降低嵌入式传感器性能的手机。此外，一些研究提出了计算负荷更高的复杂算法，这导致需要使用更多硬件并增加功耗。在此背景下，本研究旨在通过在城市巡逻全地形车（ATV）中实施的低成本专用传感器来监测道路状况，其中使用低复杂度信号处理技术进行降噪和检测算法，以优化能源消耗。结果表明，坑洼检测的平均准确率为90.5%，国际平整度指数（IRI）计算的相对误差为8.41%，监测时间平均减少65.4%。
