Road Surface Temperature Mapping from Vehicle Ambient Data
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Solution Overview
Problem
Conventional methods for estimating road surface temperature rely heavily on the availability and density of roadside weather stations, which can be limited by geographical constraints, unavailability, or restricted data access, leading to inaccurate or incomplete temperature estimates.
Innovation Solution
A method that retrieves location-specific ambient and roadside sensor data, establishes a relationship between these data sets using techniques like neural networks, and creates a data structure to estimate road surface temperature, allowing vehicles to determine temperature without relying on current roadside sensor data, even in areas with limited or no roadside weather stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If roadside weather stations are densely distributed to improve estimation accuracy, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent introduces ambient sensor data (air temperature, humidity, pressure, wind speed) as intermediary variables that mediate between roadside weather station measurements and road surface temperature estimation. These ambient conditions serve as proxies that can be measured by vehicles and used to infer road surface temperature without requiring dense deployment of specialized roadside sensors.
Solution Approach 2:
The patent creates a computational model that copies the physical relationship between ambient conditions and road surface temperature observed at locations with weather stations. This model is then applied to locations without weather stations, effectively copying the estimation capability from instrumented locations to non-instrumented locations without requiring physical sensor deployment.
2Adaptability or versatility
If roadside weather stations are deployed in remote areas to improve coverage, then geographical coverage improves, but reliability of data availability worsens
Solution Approach 1:
The system enables vehicles to self-collect ambient sensor data during normal operation without requiring external infrastructure. Each vehicle serves as its own measurement platform, collecting ambient conditions data that can be used for road surface temperature estimation at its current location, eliminating dependency on remotely deployed weather stations.
Solution Approach 2:
The patent makes ambient sensor data collected for general vehicle operations (air temperature, humidity, pressure sensors already present in vehicles) serve multiple purposes: they continue to monitor cabin/vehicle environment while also enabling road surface temperature estimation. This multi-functionality eliminates the need for dedicated roadside weather stations in remote areas.
3Adaptability or versatility
If multiple local providers and models are used to achieve worldwide coverage, then geographical coverage improves, but device complexity and data processing requirements increase
Solution Approach 1:
The patent develops a universal ambient-to-road-temperature model that functions across diverse geographical locations using the same fundamental relationship between ambient conditions and road surface temperature. This single universal model replaces the need for multiple location-specific models from different local providers, simplifying the system architecture while maintaining worldwide applicability.
4Measurement precision
If roadside weather station data is used directly for temperature estimation, then measurement precision improves, but loss of information increases when stations are unavailable
Solution Approach 1:
The system pre-establishes the relationship model between ambient sensor data and road surface temperature using data from locations with weather stations. This preliminary modeling phase captures the physical relationships, enabling the system to later estimate temperatures at locations without stations by applying the pre-established model to locally collected ambient data, preventing information loss when stations are unavailable.
Data Source
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AI summary
Methods, apparatuses, systems and computer program products are disclosed to estimate road surface temperature for a geographic location. Reference ambient sensor data indicative of ambient conditions in vicinity to mobile vehicles are retrieved. Reference roadside sensor data indicative of at least road surface temperature in vicinity to distributed roadside sensors are retrieved. A relationship is established between the reference ambient sensor data and road surface temperature, based on the reference ambient sensor data and based on the reference roadside sensor data. A data structure is created, encoding the established relationship. Further, current vehicle ambient sensor data, indicative of current ambient conditions in vicinity to the one or more vehicles, are collected at one or more vehicles. Finally, a road surface temperature estimate is determined for the geographic location, using the data structure and using the collected current vehicle ambient sensor data.