Road Surface Condition Prediction Using Shared Fleet Sensor Models
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Solution Overview
Problem
Existing vehicle control systems lack reliable information about upcoming road surface operating conditions, leading to challenges in planning adequate control actions, which can result in safety issues and under-utilization of control capacity.
Innovation Solution
A computer system that predicts road surface operating conditions by receiving a shared operating conditions model from a remote statistics processor and combining it with real-time sensor measurement data from the vehicle to determine a vehicle-specific model, allowing for accurate prediction of upcoming conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If currently measured parameter values are used as estimation of upcoming parameter values, then the system is simple to operate, but the reliability of vehicle control deteriorates due to lack of accurate upcoming condition information
Solution Approach 1:
The system performs preliminary actions by collecting and processing sensor data from multiple vehicles ahead of time to predict upcoming road surface conditions. The shared operating conditions model is built in advance using historical and real-time data, allowing the vehicle control system to have advance knowledge of future conditions without requiring complex real-time analysis during critical moments.
Solution Approach 2:
A shared operating conditions model acts as an intermediary between raw sensor data from the fleet and the vehicle control system. This model aggregates and processes data from multiple vehicles to provide refined predictions of upcoming road surface conditions, transforming dispersed individual measurements into reliable collective intelligence that improves prediction accuracy.
2Measurement precision
If a shared operating conditions model based on fleet measurements is implemented, then the prediction accuracy of upcoming operating conditions improves, but the device complexity increases due to additional data collection and processing requirements
Solution Approach 1:
The system merges sensor data from multiple vehicles in the fleet into a shared operating conditions model. By combining measurements from numerous vehicles traveling on the same or similar routes, the system creates a collective knowledge base that improves prediction accuracy for all participating vehicles, transforming individual limited data into robust fleet-wide intelligence.
Solution Approach 2:
The shared operating conditions model serves multiple functions simultaneously: it aggregates data from various sensor types (temperature, humidity, audio), processes information from multiple vehicles, generates predictions for different road surface conditions, and provides this intelligence to various vehicle control systems. This multi-functionality maximizes the value of the data collection infrastructure.
3Adaptability or versatility
If real-time sensor measurement data from multiple sources is integrated, then the adaptability of vehicle control to different road conditions improves, but the loss of time in processing and analyzing data increases
Solution Approach 1:
The shared operating conditions model is built and updated in advance using real-time sensor data from the fleet, so that when a vehicle needs predictions, the foundational model is already prepared. This preliminary data aggregation and model construction reduces the real-time processing burden during critical vehicle control decisions.
Solution Approach 2:
The system implements feedback mechanisms where sensor measurements from vehicles are continuously fed into the shared operating conditions model, which then provides updated predictions back to the vehicle control systems. This closed-loop feedback enables the system to adapt to changing road conditions dynamically while distributing the computational workload across the fleet.
Data Source
AI summary
A method includes receiving at least a portion of a shared operating conditions model from a remote statistics processor, receiving real-time sensor measurement data from sensors of the vehicle, determining a vehicle-specific operating conditions model based on the portion of the shared operating conditions model and the real-time sensor measurement data, and using the vehicle-specific operating conditions model to predict upcoming operating conditions for the vehicle. Another method includes receiving respective sensor measurement data from a plurality of vehicles, determining a shared operating conditions model based on the measurement data, and causing provision of at least a portion of the shared operating conditions model to a specific vehicle.