Vehicle Sensor Network for Real-Time Road Hazard Mapping
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Solution Overview
Problem
Existing road monitoring systems lack comprehensive, timely, and accurate methods for providing real-time information about road conditions, leading to inefficiencies and safety risks due to limited infrastructure coverage, delayed notifications, and reliance on manual reporting.
Innovation Solution
A distributed network of sensor-equipped vehicles collects and processes data using dash cameras and other sensors, applying machine learning to analyze road conditions, and generates real-time map overlays with customizable indicators, ensuring data privacy and freshness through automated preprocessing and edge/cloud processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If fixed infrastructure cameras and weather stations are used to monitor road conditions, then monitoring coverage is provided at specific locations, but coverage gaps remain for rapidly changing conditions or areas without permanent monitoring infrastructure
Solution Approach 1:
The system divides the monitoring function into two parts: fixed infrastructure provides baseline coverage at permanent locations, while mobile vehicles provide dynamic coverage for changing conditions and areas without permanent infrastructure. This segmentation allows the system to overcome the limitations of either approach alone.
Solution Approach 2:
Mobile vehicles act as intermediaries between fixed monitoring infrastructure and the broader road network. They collect data in areas without permanent infrastructure and supplement fixed camera coverage, bridging the coverage gaps that neither approach can address alone.
2Measurement precision
If manual inspection methods are used to identify road maintenance needs, then detailed road condition assessment is possible, but the process is time-consuming and costly
Solution Approach 1:
The system replaces manual mechanical inspection with automated sensor-based detection. Vehicles equipped with cameras and sensors automatically capture and analyze road conditions, eliminating the need for manual inspection while maintaining or improving assessment accuracy.
Solution Approach 2:
The system enables self-service monitoring where vehicles and road infrastructure automatically collect and report condition data without human intervention. Sensors detect road issues and the system processes information autonomously, freeing personnel from routine inspection tasks.
3Adaptability or versatility
If general weather forecasts are used for fleet operations, then broad weather information is available, but localized road conditions such as ice, snow, or flooding are not accurately reflected
Solution Approach 1:
The system transitions from uniform general weather forecasts to localized road condition data. By deploying sensors at multiple geographic locations, the system provides condition-specific information tailored to each local area, allowing fleet operators to make location-specific decisions rather than applying blanket weather precautions.
Solution Approach 2:
The system adds a spatial dimension to weather information by collecting data from multiple geographic locations simultaneously. Instead of a single regional forecast, the system provides a distributed network of localized condition reports, enabling precise location-based operational decisions.
4Adaptability or versatility
If manual reporting systems or crowd-sourced data are used to identify road hazards, then hazard information can be collected, but notifications are delayed and coverage is incomplete particularly in less-traveled areas
Solution Approach 1:
The system implements continuous automated monitoring that operates without interruption, unlike manual reporting which occurs only when someone observes and reports a hazard. Sensors continuously scan road conditions, ensuring hazards are detected immediately upon occurrence rather than when noticed by a human observer.
Solution Approach 2:
Automated sensors serve as intermediaries that detect hazards objectively and report them systematically, replacing reliance on human observation and manual reporting. This intermediary system ensures consistent coverage across all monitored areas, including less-traveled routes where human observers are unlikely to be present.
Data Source
AI summary
Systems and methods for providing real-time road condition information using a network of sensor-equipped vehicles. The system includes a plurality of vehicles equipped with dash cameras and other sensors that collect road condition data. A spatiotemporal index maintains processed sensor data associated with road segments, where personally identifiable information is removed and data quality is validated. The system analyzes the sensor data to detect various road network conditions including weather conditions, construction zones, and infrastructure status. The system monitors data freshness and automatically triggers collection of updated data when existing data exceeds age thresholds. User account information customizes the display of road conditions based on vehicle types and regulatory requirements.