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KR-102963233-B1 - ROAD DAMAGE ASSESSMENT METHOD PERFORMED BY VEHICLE-MOUNTED PLATFORM

KR102963233B1KR 102963233 B1KR102963233 B1KR 102963233B1KR-102963233-B1

Abstract

The present invention relates to a road damage assessment method performed by a vehicle-mounted platform, comprising a process of collecting impact signals from a piezo impact sensor or an IMU (Inertial Measurement Unit) and determining the signal as an impact event if the Z-axis acceleration or impact amplitude exceeds a set threshold. Subsequently, the impact event is normalized based on the vehicle's speed and weight information, and a Fast Fourier Transform (FFT) is performed on the impact signal to calculate the energy ratio of the maximum amplitude frequency f1 and the abnormal component f2. Furthermore, if the energy ratio of the abnormal component f2 is greater than a specific ratio of the total spectral energy and the maximum amplitude frequency f1 falls within a reference band, the impact signal can be determined as an impact event caused by structural damage, such as potholes or delamination of the road substructure, rather than simple road surface irregularities.

Inventors

  • 이성진
  • 이지원
  • 이도건

Assignees

  • 한국건설시험연구소 주식회사

Dates

Publication Date
20260511
Application Date
20250902

Claims (10)

  1. In a road damage assessment method performed by a vehicle-mounted platform, A step of collecting impact signals from a piezo impact sensor or an IMU (Inertial Measurement Unit), and recognizing an impact event when the Z-axis acceleration or impact amplitude exceeds a threshold; A step of normalizing the above impact event based on vehicle speed and vehicle weight; A step of performing a Fast Fourier Transform (FFT) on the above shock signal to calculate the energy ratio of the maximum amplitude frequency f1 and the abnormal component f2; A step comprising determining the impact signal as an impact event caused by structural damage, such as potholes or road subsurface peeling, rather than simple road surface irregularities, when the energy ratio of the abnormal component f2 is greater than or equal to a specified ratio to the total spectral energy and the maximum amplitude frequency f1 falls within a reference band. method.
  2. In Article 1, The above-specified ratio is approximately 25%, method.
  3. In Article 1, The above Fast Fourier Transform (FFT) further includes the step of applying a corrected standard based on vehicle-specific sensor sensitivity, road conditions, speed, etc. method.
  4. In Article 1, The above reference band includes 150Hz to 350Hz, method.
  5. In Article 1, In determining the impact event caused by structural damage such as potholes or road subsurface peeling, the method further includes the step of calculating the final road damage reliability (Rd) by multiplying by a normalized weighting index (Wi) to correct for the influence of the vehicle's driving speed (v) and load (m). method.
  6. In Article 5, The above includes an additional step of transmitting the final road damage reliability (Rd) to a control server, The above control server compares and aggregates impact events at the same point collected from other vehicles to determine road maintenance priorities based on accumulated reliability, method.
  7. In Article 6, The above control server is, A multi-sensor fusion engine that fuses multi-sensor data considering sensor reliability weights, a driving pattern fusion engine that clusters event data including abnormal driving trajectories based on spatial and temporal criteria to identify damaged road sections, a maintenance scheduler that determines maintenance priorities and establishes a schedule plan for the identified damaged road sections, and a notification layer that provides notification information to an administrator, method.
  8. In Article 6, The above control server is configured to receive data from the above vehicle-mounted platform and fixed vision node, method.
  9. In Article 8, The above fixed vision node includes a fixed camera that captures the driving trajectory of a vehicle on a road, and a trajectory analysis unit that analyzes the driving trajectory of the vehicle based on the captured image and determines an abnormal trajectory. method.
  10. In Article 8, The above control server is, Configured to automatically adjust road grades based on the type and severity of damage in identified damaged road sections, and to share the relevant information in conjunction with the traffic control system, method.

Description

Road damage assessment method performed by a vehicle-mounted platform The present invention relates to a road damage assessment method performed by a vehicle-mounted platform. Over time, road pavements are exposed to complex stresses such as traffic loads, climate and temperature changes, de-icing salts, and repeated freeze-thaw cycles, inevitably leading to deterioration such as cracks, potholes, and delamination. Traditionally, the mainstream methods involved municipal road patrol officers frequently inspecting damaged areas visually, or deploying measurement vehicles equipped with lane cameras and accelerometers to specific sections for comprehensive, precise measurements. However, visual inspections require a large workforce and suffer from a sharp decline in accuracy during nighttime or rain, while the vehicle-based method is difficult to manage in real-time due to high operating costs and long cycle times. FIG. 1 is a block diagram of a road surface management system according to one embodiment. Figure 2 is a block diagram of the sensor module shown in Figure 1. Figure 3 is a block diagram of the trajectory analysis unit shown in Figure 1. FIG. 4 is a conceptual diagram illustrating the operation of a trajectory analysis unit according to one embodiment. Figure 5 is a flowchart for detecting an abnormal trajectory that calculates the deviation (DT) of a vehicle's driving trajectory using a fixed vision node. Figure 6 is a flowchart for calculating pothole depth in reverse in real time and transmitting emergency events while driving using a piezo impact strip. The aforementioned objectives, features, and advantages are described in detail below with reference to the attached drawings, thereby enabling those skilled in the art to easily implement the technical concept of the present invention. In describing the present invention, detailed descriptions of known technologies related to the present invention are omitted if it is determined that such descriptions would unnecessarily obscure the essence of the invention. Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings. FIG. 1 is a block diagram of a road surface management system according to one embodiment. FIG. 1 schematically illustrates an overall block diagram of a road surface management system according to one embodiment of the present invention, and may include a vehicle-mounted platform (100), a fixed vision node (200), and a central server (300). The vehicle-mounted platform (100) is configured to be mounted on public or private means of transportation, such as buses, taxis, cleaning vehicles, and postal vehicles, that travel on actual roads, and acquires data based on physical interaction with the road surface. The vehicle-mounted platform (100) may include a sensor module (110) and a communication module (112). According to one embodiment of the present invention, a vehicle-mounted platform (100) may be installed in a public transportation vehicle such as a bus or taxi and is equipped with a sensor module (110) comprising a black box camera, a LiDAR sensor, a GPS module, and a tilt or displacement sensor. The sensor module (110) can continuously photograph and measure the road surface while the vehicle is traveling on the road and can acquire location information and a change in tilt in real time. In particular, for a new section where road paving has been completed, an initial reference image is stored so that it can be compared with road images repeatedly collected at the same location thereafter. In the process of acquiring a reference image, the installation height of the camera and lidar sensor is adjusted to ensure a consistent field of view and height with respect to the initial shooting time, thereby minimizing errors during subsequent comparisons. Subsequently, high-resolution images and lidar data collected during driving are converted into a bitmap with reduced pixel count or lightweight image data inside the vehicle, and transmitted to a central server (300) along with position coordinates and tilt information. Based on the received data, the central server (300) automatically detects abnormal conditions of the road, such as potholes, road surface subsidence, and cracks, by comparing GIS (Geographic Information System) information, reference images, current images, and sensor tilt data. The central server (300) automatically calculates a road grade (e.g., safety grade) for each road section based on the degree of abnormality, frequency of occurrence, and structural risk, and can preemptively determine the priority and timing of maintenance accordingly. In addition, if a sudden impact or displacement is detected while the vehicle is in motion, the system can immediately analyze the event and determine whether a pothole or manhole cover has been dislodged by referring to video information. In this case, the central server (300) can take follow-up m