Road Traction Mapping Using Distributed Vehicle Sensor Matching
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Solution Overview
Problem
Current systems lack an effective method to determine when and where snow and ice need to be removed from roadways based on road surface traction capacity, especially during adverse weather conditions.
Innovation Solution
A distributed computing system that aggregates vehicle sensor data from multiple vehicles within a common spatio-temporal zone, combining traction levels with contextual data points to determine road surface traction capacity, using perfect and partial match values calculated through similarity weights and Jaccard weights, and integrates this information with weather and API data to assess traction capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle sensor data from multiple vehicles is aggregated and processed through distributed computing systems, then the accuracy and reliability of road surface traction capacity determination is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex determination process into distinct computational components: individual vehicle sensor data collection, contextual data point generation, matching algorithms (perfect and partial matches), and aggregation at central computers. This segmentation allows each component to be optimized independently while maintaining overall system reliability.
Solution Approach 2:
The patent introduces contextual data points and matching algorithms as intermediaries between raw vehicle sensor data and final traction capacity determination. These intermediaries process and standardize data from multiple vehicles, reducing the complexity of direct multi-vehicle data integration while improving determination accuracy.
2Loss of time
If real-time data processing is implemented across multiple vehicles, then the timeliness of traction capacity assessment is improved, but the computational load and energy consumption increase
Solution Approach 1:
The system implements partial processing at vehicle level (generating contextual data points) and partial aggregation at central computer level. This distributed partial action reduces the computational burden on individual vehicles while maintaining real-time assessment capability, balancing energy consumption with timeliness.
Solution Approach 2:
Contextual data points are pre-computed and standardized at the vehicle level before transmission to central computers. This preliminary action reduces the complexity and energy requirements of real-time processing at central facilities, enabling faster aggregation and assessment.
3Measurement precision
If contextual data matching algorithms are used to compare multiple vehicles, then the precision of traction capacity measurement is improved, but the computational complexity increases
Solution Approach 1:
The matching algorithms focus on locally relevant contextual data points specific to each vehicle's operating conditions rather than comparing all possible parameters. This local quality approach improves measurement precision for relevant factors while reducing overall computational complexity.
Solution Approach 2:
The system transforms raw sensor data into standardized contextual data points with specific parameters (spatial, temporal, weather, road conditions). This parameter transformation enables efficient matching and comparison while improving measurement precision through standardized metrics.
Data Source
AI summary
A distributed computing system for determining road surface traction capacity for roadways located in a common spatio-temporal zone includes a plurality of vehicles that each include a plurality of sensors and systems that collect and analyze a plurality of parameters related to road surface conditions in the common spatio-temporal zone. The distributed computing system also includes one or more central computers in wireless communication with each of the plurality of vehicles. The one or more central computers execute instructions to determine a road surface traction capacity value for the common spatio-temporal zone.