Aerial Drone Road Surface Assessment via Spectral Analysis
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Solution Overview
Problem
Current methods for assessing road surface conditions, especially in temperate climates with rapid transitions between dry, wet, snow, and ice, are inadequate as they lack reliable forecasts for less active roads, leading to increased accident risks and costs, and existing technologies are limited in providing data for these conditions.
Innovation Solution
Deploying sensor-equipped aerial drones to assess road surface conditions using spectroradiometers and other sensors, which measure light reflection in near-infrared and short-wavelength infrared spectra, to detect dangerous conditions like ice and pooled water, and optimize flight paths using models and historical incident data to prioritize assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional road surface assessment methods are used, then infrastructure cost is reduced, but measurement coverage and reliability for less active roads deteriorates
Solution Approach 1:
The patent replaces traditional mechanical road surface assessment systems with an optical-based aerial drone system equipped with spectroradiometers. This substitution enables remote sensing of road surface conditions without physical contact, allowing comprehensive coverage of less active roads while maintaining assessment reliability through spectral analysis of reflected light
Solution Approach 2:
The patent transitions from ground-based two-dimensional assessment to aerial three-dimensional remote sensing. By viewing road surfaces from an aerial perspective using spectroradiometers, the system can efficiently assess multiple locations including less active roads, thereby improving both coverage productivity and assessment reliability simultaneously
2Reliability
If aerial drones are deployed to assess all roads including less active ones, then measurement coverage improves, but use of energy and operational cost increases
Solution Approach 1:
The patent changes the operational parameters of drone deployment by using automated prioritization algorithms that adjust flight paths and assessment frequencies based on road importance, weather conditions, and incident history. This selective approach ensures comprehensive data collection for critical roads while reducing energy consumption for less critical routes, balancing reliability improvement with energy efficiency
Solution Approach 2:
The patent applies different assessment intensities to different road segments based on their characteristics. High-priority roads receive frequent, detailed spectral analysis, while less active roads receive periodic assessments. This localized quality approach ensures data completeness where needed while minimizing unnecessary energy consumption
3Productivity
If optimized flight paths are used to prioritize high-risk locations, then assessment efficiency improves, but measurement precision for low-priority areas may deteriorate
Solution Approach 1:
The patent dynamically adjusts spectral analysis parameters based on location priority and environmental conditions. For high-priority locations, the system uses full-spectrum analysis with multiple bands to maximize detection accuracy. For low-priority areas, streamlined spectral sampling maintains adequate precision while improving overall assessment throughput through parameter optimization
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides more comprehensive and reliable road surface condition assessments, reducing accidents and costs by offering real-time data for all roads, including less active ones, and improves predictive model calibration through validation with drone observations.
Implementation Method 1
measure light reflection in near-infrared and short-wavelength infrared spectra
Implementation Method 2
measure light reflection in near-infrared and short-wavelength infrared spectra
Data Source
AI summary
Method, apparatus, and computer program product are provided for assessing road surface condition. In some embodiments, candidate locations each forecast to have a dangerous road surface condition are determined, an optimized flight path is determined comprising a sequence of sites corresponding to the candidate locations, dispatch is made to a first site within the sequence, and a road surface condition at the first site is assessed using an onboard sensor (e.g., spectroradiometer). In some embodiments, a check for new information is performed before dispatch is made to a second site. In some embodiments, the candidate locations are determined using both a model forecast and data-mined locations considered hazardous. In some embodiments, the optimized flight path is determined using TSP optimization constrained by available flight time and prioritized by frequency of historical incident and severity of forecast road surface condition.