European Commission, Joint Research Centre, 21027 Ispra, Italy.
Faculty of Electrical Engineering, Czech Technical University in Prague, 160 00 Prague, Czech Republic.
Sensors (Basel). 2020 Nov 10;20(22):6425. doi: 10.3390/s20226425.
The detection and identification of road anomalies and obstacles in the road infrastructure has been investigated by the research community using different types of sensors. This paper evaluates the detection and identification of road anomalies/obstacles using the data collected from the Inertial Measurement Unit (IMU) installed in a vehicle and in particular from the data generated by the accelerometers' and gyroscopes' components. Inspired by the successes of the application of deep learning to various identification problems, this paper investigates the application of Convolutional Neural Network (CNN) to this specific problem. In particular, we propose a novel approach in this context where the time-frequency representation (i.e., spectrogram) is used as an input to the CNN rather than the original time domain data. This approach is evaluated on an experimental dataset collected using 12 different vehicles driving for more than 40 km of road. The results show that the proposed approach outperforms significantly and across different sampling rates both the application of CNN to the original time domain representation and the application of shallow machine learning algorithms. The approach achieves an identification accuracy of 97.2%. The results presented in this paper are based on an extensive optimization both of the CNN algorithm and the spectrogram implementation in terms of window size, type of window, and overlapping ratio. The accurate detection of road anomalies/obstacles could be useful to road infrastructure managers to monitor the quality of the road surface and to improve the accurate positioning of autonomous vehicles because road anomalies/obstacles could be used as landmarks.
道路基础设施中的道路异常和障碍物的检测和识别一直是研究界使用不同类型的传感器进行研究的课题。本文评估了使用安装在车辆中的惯性测量单元（IMU）收集的数据，特别是使用加速度计和陀螺仪组件生成的数据，来检测和识别道路异常/障碍物。受到深度学习在各种识别问题中的成功应用的启发，本文研究了卷积神经网络（CNN）在这一特定问题中的应用。具体来说，我们在这方面提出了一种新的方法，即将时频表示（即声谱图）作为 CNN 的输入，而不是原始时域数据。该方法在使用 12 辆不同车辆行驶超过 40 公里的道路采集的实验数据集上进行了评估。结果表明，该方法在不同的采样率下，无论是将 CNN 应用于原始时域表示，还是应用浅层机器学习算法，都有显著的性能提升。该方法的识别准确率达到了 97.2%。本文提出的方法在 CNN 算法和声谱图实现方面都进行了广泛的优化，包括窗口大小、窗口类型和重叠率等方面。准确检测道路异常/障碍物对于道路基础设施管理者来说非常有用，因为它可以监测路面质量，并提高自动驾驶车辆的准确定位，因为道路异常/障碍物可以作为地标使用。
