Shaded Road Segment Icing Prediction Using LIDAR Sky View Models
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Solution Overview
Problem
Current road condition prediction models fail to accurately account for granular and real-time shading effects from dynamic vegetation and permanent structures, leading to unreliable warnings for hazardous road conditions, especially in areas with shaded or poorly drained sections that remain icy after inclement weather.
Innovation Solution
A system that selects road segments for prediction based on weather conditions, generates a solar radiation budget model using both permanent and dynamic structure models, and updates it with LIDAR data and vegetation growth predictions to accurately simulate ice accumulation and reduction, thereby providing precise road condition forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current road condition prediction models are used, then general road condition forecasts can be provided, but they fail to accurately account for granular shading effects from vegetation and structures, leading to unreliable warnings for hazardous conditions
Solution Approach 1:
The patent divides the road network into discrete road segments and creates individual solar radiation budget models for each segment. This segmentation allows the system to account for localized shading effects from vegetation and structures on a granular basis, improving prediction accuracy without requiring a complete overhaul of the entire modeling system. Each segment can be analyzed independently with its specific shading characteristics.
Solution Approach 2:
The patent applies local quality by incorporating segment-specific shading factors into the solar radiation budget model. Each road segment receives customized shading adjustments based on its unique characteristics, including the presence and density of vegetation, permanent structures, and dynamic objects. This localized approach enables accurate prediction of ice accumulation risks for each segment while maintaining computational efficiency.
2Reliability
If solar radiation budget models are generated for each road segment using permanent and dynamic structure models, then accurate ice accumulation prediction is achieved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing shading factors for permanent structures and vegetation in a database before road condition prediction is needed. This pre-processing step creates a ready-to-use reference system that can be quickly applied to road segments during actual prediction operations. The preliminary establishment of vegetation models and structure databases reduces real-time computational complexity while maintaining high prediction reliability.
Solution Approach 2:
The patent introduces an intermediary layer in the form of a centralized database that stores pre-processed shading information from permanent structures and dynamic vegetation. This intermediary database acts as a mediator between the complex physical reality of shading objects and the simplified computational model, allowing the system to access detailed shading characteristics without directly processing complex geometric calculations during real-time prediction.
3Measurement precision
If LIDAR data and vegetation growth predictions are integrated into the model, then granular and accurate road condition prediction is enabled, but false alerts are reduced and driver safety is improved
Solution Approach 1:
The patent performs preliminary actions by pre-processing LIDAR data to create three-dimensional models of permanent structures and pre-establishing vegetation databases with growth prediction algorithms. This pre-processing transforms raw LIDAR point clouds into structured geometric models that can be efficiently queried during road condition predictions. By performing these computationally intensive operations in advance, the system minimizes real-time processing time while maintaining high prediction precision.
Solution Approach 2:
The patent creates simplified copies of complex physical structures by generating three-dimensional geometric models from LIDAR data that replicate the shading characteristics of permanent structures. These digital copies serve as computationally efficient representations that capture the essential shading behavior without requiring complex physical calculations. The vegetation database similarly creates simplified models of dynamic vegetation that can be quickly applied to road segments.
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 enables granular and accurate prediction of road conditions, reducing false alerts and improving driver safety by providing timely and relevant warnings about hazardous ice-covered segments.
Implementation Method 1
calculating an expected solar radiation budget for the road segment based on a solar position during the time period for analysis, the geographic position of the road segment, and weather conditions associated with the road segment
Implementation Method 2
determining a permanent sky view factor for the road segment using LIDAR data points and/or a satellite snapshot of that region
Data Source
AI summary
Road condition prediction for potentially hazardous road segments is described. For roadways that may contain accumulated frozen precipitation, a road segment for road condition prediction is selected based on weather conditions. Various models including a solar radiation budget model, a permanent structures model, a dynamic structures model, and a road condition model are generated for the selected road segment and account for shading effects on the road segment caused by objects near the road segment. A road condition prediction for hazardous conditions on the road segment is determined based on the road condition model and provided to a driver to alert the driver of any potentially hazardous conditions.