Road Condition Model Using Mobile Sensor Data Normalization
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Solution Overview
Problem
Current methods for monitoring and assessing road conditions are inefficient, relying on human visual inspection and are often delayed, leading to potential injuries and damages, as well as increased insurance claims, due to the limited and time-consuming nature of traditional data collection.
Innovation Solution
A system and method utilizing mobile devices to collect and normalize road condition data, such as vibration, speed, and weather data, which is then applied to a road condition model to predict and identify issues like potholes, enabling early detection and notification of road problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional human visual inspection is used to collect road condition data, then the data collection process is simple and requires minimal technology, but the monitoring coverage is limited and the response time is delayed
Solution Approach 1:
The patent makes mobile devices serve multiple functions: they act as both communication devices for users and as road condition monitoring sensors. The mobile device's existing sensors (accelerometers, gyroscopes, GPS) are repurposed to detect road conditions, eliminating the need for dedicated monitoring equipment while expanding data collection coverage across the city.
Solution Approach 2:
The system leverages the mobile devices that citizens already carry and use daily. These devices automatically collect road condition data through their built-in sensors without requiring additional equipment or specialized training. The normalization server then processes this self-collected data from multiple device types, turning personal devices into a distributed monitoring network.
2Quantity of substance
If data is collected from multiple types of remote devices, then the quantity and diversity of road condition data increases, but the data processing complexity increases due to device-specific variations
Solution Approach 1:
The normalization server applies device-specific parameter transformations to standardize data from different mobile devices and telematics systems. Each device type has unique sensor characteristics, sampling rates, and data formats; the server adjusts these parameters to a common reference frame, enabling consistent road condition assessment across diverse data sources without manual intervention.
Solution Approach 2:
The normalization server acts as an intermediary layer between heterogeneous remote devices and the road condition model. It receives raw data from various device types, applies appropriate normalization rules for each device, and outputs standardized data suitable for model input. This intermediary process automatically handles the complexity of multi-device data integration.
3Measurement precision
If real-time road condition monitoring is implemented using mobile devices, then the detection speed and accuracy of road issues improves, but the computational resources and energy consumption increase
Solution Approach 1:
The system divides the computational workload between the mobile device and the normalization server. The mobile device performs local data collection and preliminary processing using its onboard sensors and processor, then transmits only the processed data to the server. This segmentation reduces the energy burden on battery-powered mobile devices while maintaining high detection accuracy through server-side model processing.
Solution Approach 2:
The system uses the mobile device's existing sensors and processing capabilities to the extent available, then supplements with server-side computation for the remaining analytical work. Rather than requiring full real-time processing on the device, the system performs partial processing locally and completes the analysis on the server, balancing accuracy requirements with energy constraints.
Data Source
AI summary
Systems and methods for monitoring and assessing road conditions are disclosed herein that receive raw data comprising information indicative of road conditions from various types of remote devices. The road condition data may be normalized based on the type of the remote device and the normalized road condition data may be stored. A road condition model may be generated based on the normalized road condition data and using the device location. The road condition model may be used to identify pothole locations, rate road segments, etc. The road condition model may apply various data set weightings and learning algorithms to present an accurate result. The road condition model may be optimized based on feedback from city personnel and other sources of information to determine the accuracy of the model outputs. The road condition model may also be trained to ensure accuracy.