Road Sentinel AI Pylon Meshed Hazard Detection
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Solution Overview
Problem
Current road signs and traffic management systems lack real-time monitoring and awareness of vehicles and environmental conditions, leading to inefficiencies in traffic flow, safety, and self-driving technology adoption, with limited advance notice of hazards and no integration of AI for predictive navigation.
Innovation Solution
The Road Sentinel AI Pylon employs a meshed aware road system combining roadside sensors with AI and machine learning to provide real-time, precise data on road conditions, enhancing driver assistance, personal security, and traffic management, using non-GPS geo-fencing and proprietary algorithms for hazard detection and emergency response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional road signs and traffic management systems are used, then device complexity is low, but real-time monitoring capability and awareness of vehicles and environmental conditions are insufficient
Solution Approach 1:
The patent combines multiple functions (sensing, processing, communication, and control) into an integrated roadside unit that monitors vehicles, pedestrians, cyclists, and environmental conditions. This merging of previously separate systems enables comprehensive real-time monitoring while managing complexity through unified architecture.
Solution Approach 2:
The roadside unit is designed as a multi-functional system that simultaneously performs vehicle detection, pedestrian detection, cyclist detection, environmental monitoring, and communication with various stakeholders. This universal design allows a single system to address multiple traffic safety and management needs.
2Measurement precision
If AI and machine learning are integrated for predictive navigation and hazard detection, then safety and navigation accuracy improve, but device complexity and computational requirements increase
Solution Approach 1:
The system uses AI and machine learning to predict potential hazards and navigation needs before they occur. By analyzing historical and real-time data, the system proactively identifies risks and prepares navigation recommendations, improving safety while distributing computational load over time.
Solution Approach 2:
The roadside unit acts as an intermediary between raw sensor data and navigation decisions, using AI algorithms to process and interpret information. This intermediary layer enables complex predictive analytics while shielding the overall system from excessive computational complexity at any single point.
3Loss of information
If comprehensive sensor networks are deployed for meshed aware road system, then real-time data accuracy and coverage improve, but infrastructure cost and system complexity increase
Solution Approach 1:
The comprehensive monitoring system is divided into discrete roadside units deployed at specific locations along the road network. Each unit handles local sensing and processing independently, with results aggregated to form complete road condition data. This segmentation enables scalable deployment while maintaining data completeness.
Solution Approach 2:
The system implements feedback loops where sensor data from the meshed network continuously informs navigation and safety decisions, which are then communicated back to users. This feedback mechanism ensures that the comprehensive data collection translates into actionable information while optimizing sensor utilization.
4Loss of time
If real-time hazard detection and emergency response systems are implemented, then emergency response time improves, but system complexity and processing requirements increase
Solution Approach 1:
The system detects potential hazards and emergency conditions before they fully develop, allowing preventive actions to be taken. By identifying risks early through continuous monitoring and AI analysis, the system can alert users and initiate emergency protocols before situations deteriorate, reducing response time while managing processing complexity through early detection.
Data Source
AI summary
Improvements in a road sentinel AI pylon that monitors the area around the pylon(s) to map out the area and movement within the area surround the pylon(s) for hazard detection and monitoring. Each pylon can be set up, moved, or relocated and in addition to the sensors include a display to aid drivers. The pylons create a meshed mobile advances personal security system that can provide awareness of road system to improve traffic with a traffic flow management system. The pylons can detect a road hazard using grid road extraction and detection, with wrong way driver detection. The system further includes a mobile advanced personal security system that helps with finding individuals that are lost or have dementia, it can also aid in helping individuals that may be non-responsive.